High-dimensional Data Types Research Articles

Fast and universal single-molecule localization using multi-dimensional point spread functions

The recent development of single-molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information about single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole orientation). However, sophisticated high dimensional point spread function (PSF) modeling and analyzing methods have greatly impeded the broad accessibility of these techniques. Here, we present a graphics processing unit (GPU) -based B-spline PSF modeling method that could flexibly model high dimensional PSFs with arbitrary shape without greatly increasing the model parameters. Our B-spline fitter achieves 100 times speed improvement and minimal uncertainty for each dimension, enabling efficient high dimensional single-molecule analysis. We demonstrated, both in simulations and experiments, the universality and flexibility of our B-spline fitter to accurately extract the abundant information from different types of high dimensional single-molecule data, including multicolor PSF (3D + color), multi-channel four-dimensional 4Pi-PSF (3D + interference phase) and five-dimensional vortex PSF (3D + dipole orientation).

Temporal Autoregressive Matrix Factorization for High-Dimensional Time Series Prediction of OSS.

Open-source software (OSS) plays an increasingly significant role in modern software development tendency, so accurate prediction of the future development of OSS has become an essential topic. The behavioral data of different open-source software are closely related to their development prospects. However, most of these behavioral data are typical high-dimensional time series data streams with noise and missing values. Hence, accurate prediction on such cluttered data requires the model to be highly scalable, which is not a property of traditional time series prediction models. To this end, we propose a temporal autoregressive matrix factorization (TAMF) framework that supports data-driven temporal learning and prediction. Specifically, we first construct a trend and period autoregressive model to extract trend and period features from OSS behavioral data, and then combine the regression model with a graph-based matrix factorization (MF) to complete the missing values by exploiting the correlations among the time series data. Finally, use the trained regression model to make predictions on the target data. This scheme ensures that TAMF can be applied to different types of high-dimensional time series data and thus has high versatility. We selected ten real developer behavior data from GitHub for case analysis. The experimental results show that TAMF has good scalability and prediction accuracy.

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.

A review on the microgrid sizing and performance optimization by metaheuristic algorithms for energy management strategies

A growing concern over climate change and the depletion of conventional energy resources have led to the urgent need for sustainable and resilient energy solutions. The optimization of the size of renewable sources is crucial to maximizing their effectiveness. In contrast to conventional single-objective optimization, the multi-objective technique aims to achieve a trade-off between energy cost and power supply reliability. Due to this need, microgrids (MG) have emerged as a promising paradigm, allowing for localized and decentralized energy generation and distribution. Consequently, the conventional techniques for modelling and optimizing exhibit numerous limitations as the power grid continues to produce substantial volumes of high-dimensional and diverse data types. This review paper examines the use of metaheuristic algorithms in the context of multi-objective energy optimization for hybrid renewable energy-integrated microgrids. A comparative analysis of diverse metaheuristic algorithms for microgrid optimization is provided in this paper, which emulates natural phenomena, such as evolutionary processes and swarm dynamics. Based on the findings of case studies, it can be concluded that trade-offs exist between various objectives, eventually leading to the development of both resilient and efficient microgrid designs. By reviewing sustainable energy solutions, and advocating microgrids as viable alternatives to conventional centralized power systems, the review enhances the advancement of sustainable energy solutions.

Adaptive Sparse Multi-Block PLS Discriminant Analysis: An Integrative Method for Identifying Key Biomarkers from Multi-Omics Data.

With the growing use of high-throughput technologies, multi-omics data containing various types of high-dimensional omics data is increasingly being generated to explore the association between the molecular mechanism of the host and diseases. In this study, we present an adaptive sparse multi-block partial least square discriminant analysis (asmbPLS-DA), an extension of our previous work, asmbPLS. This integrative approach identifies the most relevant features across different types of omics data while discriminating multiple disease outcome groups. We used simulation data with various scenarios and a real dataset from the TCGA project to demonstrate that asmbPLS-DA can identify key biomarkers from each type of omics data with better biological relevance than existing competitive methods. Moreover, asmbPLS-DA showed comparable performance in the classification of subjects in terms of disease status or phenotypes using integrated multi-omics molecular profiles, especially when combined with other classification algorithms, such as linear discriminant analysis and random forest. We have made the R package called asmbPLS that implements this method publicly available on GitHub. Overall, asmbPLS-DA achieved competitive performance in terms of feature selection and classification. We believe that asmbPLS-DA can be a valuable tool for multi-omics research.

An advanced variable selection method based on information gain and Fisher criterion reselection iteration for multivariate calibration

The difficulty of analyzing high-dimensional data makes dimensionality reduction essential, and variable selection is widely used as a dimensionality reduction method to improve the interpretability of models. A variable selection method based on information gain and Fisher's criterion reselection iteration (IFRI) is proposed in this paper. It selects variables that have strong associations with the property of interest by two classical feature selection functions to improve interpretability of the selected variables. At first, information gain was employed to pre-select “global” feature variables with strong ability to distinguish between different classes, and Fisher's criterion was applied to re-select key variables further from the selected “global” variables that enable larger inter-class variance and smaller intra-class variance for the samples. Pearson correlation coefficients were then applied to eliminate variables with strong linear correlation and a series of subsets were obtained iteratively. At the end, cross-validation (CV) was used to calculate the obtained subset, and the one with the lowest root mean square error of cross-validation (RMSECV) was considered optimal. Combining with partial least squares (PLS), it was applied to the analysis of nicotine and total sugar content in tobacco samples. By comparing and validating with two representative variable selection algorithms, CARS and MC-UVE, IFRI has been demonstrated to be an effective and applicable method to establish high-performance models by selecting a few key variables. Differing from most methods, IFRI does not need to depend on the PLS model parameters in the process of variable selection, which makes it more interpretable. Moreover, the selected variables by IFRI are reproducible for the same dataset. Apparently, the principle of the method makes it not limited to spectral data and can be applied to the variable selection of different types of high-dimensional data by combining it with other modeling methods, for example it can be combined with qualitative discriminant models to classify tobacco or used to select critical chemical components affecting the tobacco quality.

CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints

BackgroundFinding a globally optimal Bayesian Network using exhaustive search is a problem with super-exponential complexity, which severely restricts the number of variables that can feasibly be included. We implement a dynamic programming based algorithm with built-in dimensionality reduction and parent set identification. This reduces the search space substantially and can be applied to large-dimensional data. We use what we call ‘generational orderings’ based search for optimal networks, which is a novel way to efficiently search the space of possible networks given the possible parent sets. The algorithm supports both continuous and categorical data, as well as continuous, binary and survival outcomes.ResultsWe demonstrate the efficacy of our algorithm on both synthetic and real data. In simulations, our algorithm performs better than three state-of-art algorithms that are currently used extensively. We then apply it to an Ovarian Cancer gene expression dataset with 513 genes and a survival outcome. Our algorithm is able to find an optimal network describing the disease pathway consisting of 6 genes leading to the outcome node in just 3.4 min on a personal computer with a 2.3 GHz Intel Core i9 processor with 16 GB RAM.ConclusionsOur generational orderings based search for optimal networks is both an efficient and highly scalable approach for finding optimal Bayesian Networks and can be applied to 1000 s of variables. Using specifiable parameters—correlation, FDR cutoffs, and in-degree—one can increase or decrease the number of nodes and density of the networks. Availability of two scoring option—BIC and Bge—and implementation for survival outcomes and mixed data types makes our algorithm very suitable for many types of high dimensional data in a variety of fields.

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar (3D)

There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions, and evaluate our approach through case studies of an open-pit mine truck dataset. The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

TMIC-30. EFFICIENT, QUANTITATIVE MAPPING OF TUMOR-IMMUNE NEIGHBORHOOD COMPOSITION IN GLIOBLASTOMA USING CASSATT

Abstract The microenvironment surrounding brain cancer cells, and their position relative to anatomical features such as the vasculature, is a critical contributor to tumor malignancy. Immunohistochemical staining is widely used to examine such multicellular interactions, and approaches like cyclic immunohistochemistry (cycIHC), multiplexed immunofluorescence, or mass-based imaging can include up to 100 channels of interest. However, large (often > 50 GB) datasets from cycIHC or comparable approaches currently require an expert data scientist to concatenate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of expensive proprietary software. We generated an open-source, unified, and user-friendly workflow for processing and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and scores marker expression on each detected cell. Beyond straightforward data analysis outputs such as dimensionality reduction, clustering, cell population identification and quantification, CASSATT explores the spatial relationships between cell populations via a statistical analysis module that can be applied to other high-dimensional imaging data types. In brief, it helps users identify populations of cells that interact - or do not interact - at frequencies that are greater than those occurring by chance. It also identifies specific ‘neighborhoods’ based on the cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. Within a newly generated cycIHC dataset consisting of 6 glioblastoma tissue sections processed through 8 cycles of chromogenic IHC staining, CASSATT successfully identified populations detected in parallel flow cytometry analyses and revealed specific associations between tumor and immune populations. Additional successful benchmarking was completed on a published tissue microarray dataset consisting of 107 cores processed through 18 cycles of staining and imaging.

Characterizing node-negative non-small cell lung cancer patients with similarity networks: A CancerLinQ Discovery analysis.

e20585 Background: Patients with node-negative non-small cell lung cancer (NSCLC) whose tumors are completely resected account for approximately 17% of all patients with lung cancer, and disease recurrence occurs in approximately 1 in 5 of these patients. The lack of consensus on factors associated with risk of disease recurrence among node-negative NSCLC patients is a significant barrier to applying precision medicine strategies in this patient population. Clustering similar patients based on distances between various features of data is an emerging topic in precision medicine. Patient similarity networks represent a new model for clustering patients based on heterogeneous data, whereby any data type is converted into a similarity network by defining a similarity measure. The objective of this study was to examine the utility of patient similarity networks to identify NSCLC patients at higher risk of adverse outcomes. Methods: We conducted a retrospective, observational study of 6,020 node-negative NSCLC patients with an initial diagnosis in 2011-2014 in the CancerLinQ Discovery database. A patient similarity network was assembled based on comorbidities and network communities of patients with similar comorbidities at diagnosis were identified. Using Cox proportional-hazards modeling, we examined the extent to which patient age, sex, race, ethnicity, and network community predicted 2-year disease recurrence and 2-year mortality. Results: In the adjusted analyses, patients in the network community enriched for renal disease and congestive heart failure had an 83% increased risk of mortality (95% CI = 1.39-2.41). Patients in the network community enriched for pulmonary disease had a 37% increased risk of mortality (95% CI = 1.06-1.74) yet a lower risk of recurrence (HR = 0.5, 95% CI = 0.34-0.75). After adjusting for comorbidity network community, male patients had a 14% increased risk of mortality (95%CI = 1.02-1.28) and a 21% increased risk of recurrence (95% CI = 1.05-1.40) and black patients had a lower mortality risk (HR = 0.71, 95% CI = 0.58-0.86). Conclusions: Future studies applying patient similar networks to integrate additional diverse and high dimensional data types may afford more clarity in assigning risk of adverse outcomes.

A regularization method for linking brain and behavior.

In a world of big data and computational resources, there has been a growing interest in further validating computational models of decision making by subjecting them to more rigorous constraints. One prominent area of study is model-based cognitive neuroscience, where measures of neural activity are explained and interpreted through the lens of a cognitive model. Although some early work has developed the statistical framework for exploiting the covariation between brain and behavior through factor analysis linking functions, current methods are still far from providing parsimonious accounts of high-dimensional (e.g., voxel-level) data. In this article, we contribute to this endeavor by investigating the fidelity of regularization methods such as the Lasso. Here, a combination of local and global penalty terms are applied to pressure elements of the factor loading matrix toward zero, reducing the false alarm rate. Such penalties facilitate the emergence of parsimonious network structure in the study of neural activation, giving way to clearer interpretations of high-dimensional data. We show through a set of three simulation studies and one application to real data that the Lasso can be an effective regularization method in the context of linking complex patterns of brain data to theoretical explanations of decisions. Although our analyses are specific to linking brain to behavior, the structure of the model is invariant to the type of high-dimensional data under investigation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction.

Multiomics cancer profiles provide essential signals for predicting cancer survival. It is challenging to reveal the complex patterns from multiple types of data and link them to survival outcomes. We aim to develop a new deep learning-based algorithm to integrate three types of high-dimensional omics data measured on the same individuals to improve cancer survival outcome prediction. We built a three-dimension tensor to integrate multi-omics cancer data and factorized it into two-dimension matrices of latent factors, which were fed into neural networks-based survival networks. The new algorithm and other multi-omics-based algorithms, as well as individual genomic-based survival analysis algorithms, were applied to the breast cancer data colon and rectal cancer data from The Cancer Genome Atlas (TCGA) program. We evaluated the goodness-of-fit using the concordance index (C-index) and Integrated Brier Score (IBS). We demonstrated that the proposed tight integration framework has better survival prediction performance than the models using individual genomic data and other conventional data integration methods. https://github.com/jasperzyzhang/DeepTensorSurvival. Supplementary data are available at Bioinformatics online.

Genetic Algorithm for Variable Selection and Parameter Optimization in SVM and Fuzzy SVM for Colon Cancer Microarray Classification

Colon cancer is the second leading cause of cancer-related deaths in the world, hence research on that topic needs to be undertaken with improvement. Recent advanced in microarray technology allows the monitoring of the expression level of a large set of genes simultaneously. Microarray data is a type of high-dimensional data with hundreds or even thousands number of genes (features), while usually the number of patients observed (observations) is much smaller than the number of features. This study uses a colon cancer microarray dataset contains two class of genes, normal and tumor. The aims of this study is to develop a classification model using fuzzy support vector machines (FSVM) hybridized with genetic algorithm (GA) for classifying individuals based on gene expression. Fuzzy memberships was used in SVM in order to deal with the case of imbalanced microarray data. Meanwhile, the role of genetic algorithm is, firstly, to select the relevant genes as the features and, secondly, to optimize the parameter of FSVM as GA is able to handle the problem of nonlinear optimization that has a high dimension, adaptable, and easily combined with other methods. The classification using FCBF selection has a higher accuracy value than the ones without the selection. The results also show that FSVM that has been optimized using GA has the highest accuracy value compared to other classification methods used in this study.

Automated Disease Classification Using Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) Data with Transparent Artificial Intelligence (AI)

Background: Currently, hematologic neoplasms are diagnosed using a combination of methods, which require complex equipment and highly skilled clinical laboratory scientists and technicians - scarce resources.WGS and WTS could streamline this process and become a singular method. Interpretation of WGS and WTS data in a diagnostic setting is extremely challenging due to the breadth of data and its high-dimensional data types. AI will be mandatory to identify clinically meaningful genetic patterns and produce unbiased diagnosis.Aim: Compute leukemia diagnosis using AI methods with WGS and WTS data only, depicting relevant features for a decision and thus making its results comprehensible and transparent to humans.Methods: To train the model we used our cohort of 4,689 samples both with WGS (100x coverage, 2x151bp) and WTS (50 mio reads/sample, 2x101bp), along with our independent final routine diagnosis based on gold standard techniques (GST) and following WHO guidelines. Single nucleotide variants (SNV), structural variants (SV) and copy number alterations (CNA) from WGS data using a tumor w/o normal pipeline and gene fusions (GF) and gene expression (GE) from WTS were extracted. The cohort comprised of 30 different neoplasms and was severely imbalanced (n: 20 - 773).To test its performance another independent cohort which was not used during model creation (n=202, 22 entities) was selected.Results: We trained an ensemble of multi-class classifier using SageMaker (AWS, Seattle, WA) based on LGBM implementation of gradient boosted decision trees (Ke et al, 2017) in a one vs. rest architecture (1vRA). The model accuracy reached 85% overall on a 5-fold cross-validation (Fig 1a). Since neighboring disease types such as MGUS/MM, MDS-EB-2/AML/CMML are in some cases difficult to classify correctly using GST, we trained entity-specific classifiers operating independently. Rather than forcing a single predicted class to be predicted as overwhelmingly likely, this architecture accounts for ambiguous entities. In addition to reflecting biological similarity, the 1vRA resulted in improved probability calibration, so that cases with ambiguous leukemias are more easily identifiable by the distribution of predicted class probabilities and flagged for a human. Expected calibration error was only 3.8% for the 1vRA with entity-specific components, compared to 8.7% for a single LGBM model.Typically, AI methods are black boxes, making it hard for a medical professional to understand predictions, which results in low confidence and acceptance of such systems. Thus, we particularly focused on the transparency aspect of the model. We employed the SHAP library (Lundberg et al, 2017) to retrace the models output and gain insight into the features (i.e. which SNV, SV, GE etc.) predominantly driving classification results (e.g. LPL case Fig 1b). Fig. 1c illustrates the application of SHAP at the global cohort level for two individuals wrongly diagnosed with CML compared to CML correctly predicted cohort. By using a decision plot, we can observe which features are the most important contributors to the model's prediction. Fig 1c shows that predictions for CML are primarily driven by the BCR-ABL1 features, as expected.In our independent test cohort the following entities reached a very high concordance, such as AML (16/21) AUL (11/12), BCP-ALL (10/10), CML (13/13), HZL (8/8), MGUS (7/7), Multiple Myeloma (9/11), PNH (10/10), T-ALL (6/7). Other clear cut entities with correct high level predictions include BPDCN, FL, LPL, PPBL, NK-cell, HCL-variant and HGBL.In other entities such as T-NHL results were more heterogeneous, but this was also expressed in the probability scores given by the model. The first choice had a probability score of ~50%, exposing the correct diagnosis as the second likeliest one with ~40%. Test cohort included cases with mixed diagnostic characteristics, e.g. MDS/MPN-RS-T (4/11 correct, 4 predicted as MDS, and 3 as MPN).Conclusion: We present an AI tool to interpret WGS and WTS data aiming to predict the final diagnosis without any human input and high concordance to today's WHO classification. Due to the high data dimensionality of WGS and WTS data, an impossible feat for a human. The tool is exposed via a web application and visualizations make the automated decisions transparent and verifiable through humans paving a way for better adoption of WGS and WTS into a clinical routine setting. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Knowledge discovery from gene expression dataset using bagging lasso decision tree

<p>Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.</p>

The use of high-throughput phenotyping in genomic selection context

Abstract: One of the biggest challenges that breeders face is the development of improved cultivars in changing climate conditions posing extra challenges to their labor. On the other hand, the availability of data generated with automated systems offers an opportunity to characterize genetically and phenotypically genotypes with high detail. Modern sequencing technologies delivering hundreds of thousands of molecular makers, offered the opportunity of selecting genotypes without the need of observing these in fields and this methodology was coined as Genomic Selection (GS). More recently, sophisticated automated phenotyping platforms depending on sensors able to measure a large number of plant features were also developed and have shown potential in plant breeding applications. These modern phenotyping systems that attempt to efficiently deliver phenotypic information on secondary traits are also know as high-throughput phenotyping platforms (HTPPs). The integration of HTPP with GS models opened a new research front to improve the efficiency of the selection methods based on genomic data only, specially of those traits depending on a large number of genes with small effects (complex traits). However, there are still remaining some issues to solve for developing a robust methodology able to combine in an efficient and informed way these two high dimensional data types. In this document, we provide an overview of the statistical analysis of the data derived of the HTTPs for improving the predictive ability of conventional GS models. We provide a brief introduction showing the utility of genomic data in plant breeding applications. After, we provide an overview of the field-based HTPPs considering the light detection and ranging and the unmanned aerial vehicles and how the image data derived from these platforms can be used to accelerate genetic gains. After that, we discuss about the extension of the conventional GS models to allow the incorporation of data derived of the HTPPs as main effects and also in interaction with environmental factors. The availability of several sources of information have opened a venue to investigate besides the univariate or single trait model, models based on multiple traits and also models that consider multiple time measures allowing longitudinal GS studies. Finally, we provide some conclusions as well as we mention some the current issues that do not allow to fully exploit the potential of HTTPs in plant breeding applications.

Directionally dependent multi-view clustering using copula model.

Recent developments in high-throughput methods have resulted in the collection of high-dimensional data types from multiple sources and technologies that measure distinct yet complementary information. Integrated clustering of such multiple data types or multi-view clustering is critical for revealing pathological insights. However, multi-view clustering is challenging due to the complex dependence structure between multiple data types, including directional dependency. Specifically, genomics data types have pre-specified directional dependencies known as the central dogma that describes the process of information flow from DNA to messenger RNA (mRNA) and then from mRNA to protein. Most of the existing multi-view clustering approaches assume an independent structure or pair-wise (non-directional) dependence between data types, thereby ignoring their directional relationship. Motivated by this, we propose a biology-inspired Bayesian integrated multi-view clustering model that uses an asymmetric copula to accommodate the directional dependencies between the data types. Via extensive simulation experiments, we demonstrate the negative impact of ignoring directional dependency on clustering performance. We also present an application of our model to a real-world dataset of breast cancer tumor samples collected from The Cancer Genome Altas program and provide comparative results.

Efficient nearest neighbors methods for support vector machines in high dimensional feature spaces

In the context of support vector machines, identifying the support vectors is a key issue when dealing with large data sets. In Camelo et al. (Ann Oper Res 235:85–101, 2015), the authors present a promising approach to finding or approximating most of the support vectors through a procedure based on sub-sampling and enriching the support vector sets by nearest neighbors. This method has been shown to improve the computational efficiency of support vector machines on large data sets with low or intermediate feature space dimension. In the present article we discuss ways of adapting the nearest neighbor enriching methodology to the context of very high dimensional data, such as text data or other high dimensional data types, for which nearest neighbor queries involve, in principle, a high computational cost. Our approach incorporates the proximity preserving order search algorithm of Chavez et al. (MICAI 2005: advances in artificial intelligence, Springer, Berlin, pp 405–414, 2005), into the nearest neighbor enriching method of Camelo et al. (2015), in order to adapt this procedure to the high dimension setting. For the required set of pivots, both random pivots and the base prototype pivot set of Mico et al. (Pattern Recogn Lett 15:9–17, 2015), are considered. The methodology proposed is evaluated on real data sets.

A wide dataset of ear shapes and pinna-related transfer functions generated by random ear drawings.

Head-related transfer function individualization is a key matter in binaural synthesis. However, currently available databases are limited in size compared to the high dimensionality of the data. In this paper, the process of generating a synthetic dataset of 1000 ear shapes and matching sets of pinna-related transfer functions (PRTFs), named WiDESPREaD (wide dataset of ear shapes and pinna-related transfer functions obtained by random ear drawings), is presented and made freely available to other researchers. Contributions in this article are threefold. First, from a proprietary dataset of 119 three-dimensional left-ear scans, a matching dataset of PRTFs was built by performing fast-multipole boundary element method (FM-BEM) calculations. Second, the underlying geometry of each type of high-dimensional data was investigated using principal component analysis. It was found that this linear machine-learning technique performs better at modeling and reducing data dimensionality on ear shapes than on matching PRTF sets. Third, based on these findings, a method was devised to generate an arbitrarily large synthetic database of PRTF sets that relies on the random drawing of ear shapes and subsequent FM-BEM computations.

A Novel Separating Hyperplane Classification Framework to Unify Nearest-Class-Model Methods for High-Dimensional Data.

In this article, we establish a novel separating hyperplane classification (SHC) framework to unify three nearest-class-model methods for high-dimensional data: the nearest subspace method (NSM), the nearest convex hull method (NCHM), and the nearest convex cone method (NCCM). Nearest-class-model methods are an important paradigm for the classification of high-dimensional data. We first introduce the three nearest-class-model methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this article by discovering the relationship between a convex cone and its polar cone. We then establish the new SHC framework to unify the nearest-class-model methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has a better performance than others on certain data sets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data: the spectroscopic data and the face image data.