Unsupervised Dimensionality Reduction Research Articles

UMAP for Geospatial Data Visualization

In this paper, we examine the efficacy of unsupervised learning approaches, particularly clustering and dimensionality reduction techniques, in practical applications such as image compression and geospatial data visualization. Initially, we scrutinize the applicability of the elbow rule in determining the optimal number of clusters within synthetic datasets of varying structures using the k-means algorithm. Subsequently, we evaluate the potency of density-based (DBSCAN) and hierarchical clustering algorithms in unearthing intrinsic patterns within these datasets. Shifting our focus towards practical applications, we leverage the k-means clustering for image compression, demonstrating a notable reduction in storage requirements without a significant compromise in visual quality. Our findings articulate that a strategic selection of cluster numbers can yield substantial compression rates. In the final segment of our investigation, we delve into the utility of dimensionality reduction techniques, specifically t-SNE and UMAP, in the realm of geospatial data visualization. Utilizing a dataset comprising distances between Spanish provinces, we gauge the proficiency of these techniques in preserving relative distances upon projection onto a two-dimensional plane. Our observations denote that both t-SNE and UMAP can generate precise visual representations of the original geographic layouţ with UMAP exhibiting exceptional performance in comparison with alternative methodologies. On a broader scale, our study underscores the versatility and practical utility of unsupervised learning techniques across an array of applications, ranging from image compression to geospatial data visualization. It emphasizes the significance of comprehending their foundational mechanisms and fine-tuning their hyperparameters to achieve optimal performance.

A Time Series Regression Model via Improved PCA and Bagging Algorithms

Considering the case that the prediction variable is a time series and the response variable is a continuous scalar, we propose a time series regression model based on improved PCA and Bagging Algorithms. Compared with PCA dimension reduction, the proposed method uses distance correlation coefficient matrix instead of Person correlation coefficient matrix, which makes the distribution assumption of original variables more free. Considering that PCA is an unsupervised dimension reduction technique and the connection functions between principal components and response variables are unknown, we propose to use Bagging Algorithmss to capture information of principal components related to response variables. In the actual data analysis, the comparative methods are LASSO and PCA-based linear models, and the empirical results show that the proposed method has certain competitiveness compared with the comparison method.Finally, because the base-model of Bagging Algorithms is model-free, some machine learning methods with higher precision and flexibility can be used as the base-model for data tasks with different complexity.

Comparison of dimension reduction methods for the identification of heart-healthy dietary patterns

Most nutritional epidemiology studies investigating diet-disease trends use unsupervised dimension reduction methods, like principal component regression (PCR) and sparse PCR (SPCR), to create dietary patterns. Supervised methods, such as partial least squares (PLS), sparse PLS (SPLS), and Lasso, offer the possibility of more concisely summarizing the foods most related to a disease. In this study we evaluate these five methods for interpretable reduction of food frequency questionnaire (FFQ) data when analyzing a univariate continuous cardiac-related outcome via a simulation study and data application. We also demonstrate that to control for covariates, various scientific premises require different adjustment approaches when using PLS. To emulate food groups, we generated blocks of normally distributed predictors with varying intra-block covariances; only nine of 24 predictors contributed to the normal response. When block covariances were informed by FFQ data, the only methods that performed variable selection were Lasso and SPLS, which selected two and four irrelevant variables, respectively. SPLS had the lowest prediction error, and both PLS-based methods constructed four patterns, while PCR and SPCR created 24 patterns. These methods were applied to 120 FFQ variables and baseline body mass index (BMI) from the Multi-Ethnic Study of Atherosclerosis, which includes 6814 participants aged 45-84, and we adjusted for age, gender, race/ethnicity, exercise, and total energy intake. From 120 variables, PCR created 17 BMI-related patterns and PLS selected one pattern; SPLS only used five variables to create two patterns. All methods exhibited similar predictive performance. Specifically, SPLS’s first pattern highlighted hamburger and diet soda intake (positive associations with BMI), reflecting a fast food diet. By selecting fewer patterns and foods, SPLS can create interpretable dietary patterns while maintaining predictive ability.

Two New Indices for Unsupervised Dimensionality Reduction of Hyperspectral Data

Hyperspectral imaging, with hundreds of spectral bands provide a rich data source for thematic mapping. However, the high dimensionality of these images is a challenge which is commonly treated by dimensionality reduction techniques. In this paper, a pixel-based hyperspectral dimensionality reduction technique is proposed in which the spectral signature curve (SSC) is divided into segments with equal lengths. Afterwards two new mathematical indices, which are based on integral and L2-norm, are proposed to transform each SSC segment into a new feature of the reduced feature vector. The proposed method considers the ordinance of SSC and, unlike PCA, can be applied individually to all pixels independently and simultaneously which means the method is applicable in parallel processing. The proposed data reduction technique was applied to two well-known agricultural hyperspectral scenes and was compared to wavelet and PCA data reduction techniques. The obtained results proved the efficiency of the proposed method from both classification accuracy and processing time aspects.

Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites.

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods.

Classification at the accuracy limit: facing the problem of data ambiguity

Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering work best when the input vectors are distributed over the data space in a highly non-uniform way. These tasks become however challenging in weakly structured data sets, where a significant fraction of data points is located in between the regions of high point density. We derive the theoretical limit for classification accuracy that arises from this overlap of data categories. By using a surrogate data generation model with adjustable statistical properties, we show that sufficiently powerful classifiers based on completely different principles, such as perceptrons and Bayesian models, all perform at this universal accuracy limit under ideal training conditions. Remarkably, the accuracy limit is not affected by certain non-linear transformations of the data, even if these transformations are non-reversible and drastically reduce the information content of the input data. We further compare the data embeddings that emerge by supervised and unsupervised training, using the MNIST data set and human EEG recordings during sleep. We find for MNIST that categories are significantly separated not only after supervised training with back-propagation, but also after unsupervised dimensionality reduction. A qualitatively similar cluster enhancement by unsupervised compression is observed for the EEG sleep data, but with a very small overall degree of cluster separation. We conclude that the handwritten letters in MNIST can be considered as ’natural kinds’, whereas EEG sleep recordings are a relatively weakly structured data set, so that unsupervised clustering will not necessarily re-cover the human-defined sleep stages.

Max–Min Robust Principal Component Analysis

Principal Component Analysis (PCA) is a powerful unsupervised dimensionality reduction algorithm, which uses squared ℓ2-norm to cleverly connect reconstruction error and projection variance, and those improved PCA methods only consider one of them, which limits their performance. To alleviate this problem, we propose a novel Max–Min Robust Principal Component Analysis via binary weight, which ingeniously combines reconstruction error and projection variance to learn projection matrix more accurately, and uses ℓ2-norm as evaluation criterion to make the model rotation invariant. In addition, we design binary weight to remove outliers to improve the robustness of model and obtain the ability of anomaly detection. Subsequently, we exploit an efficient iterative optimization algorithm to solve this problem. Extensive experimental results show that our model outperforms related state-of-the-art PCA methods.

Local Linear Embedding with Adaptive Neighbors

Dimensionality reduction is one of the most important techniques in the field of data mining. It embeds high-dimensional data into a low-dimensional vector space while keeping the main information as much as possible. Locally Linear Embedding (LLE) as a typical manifold learning algorithm computes neighborhood preserving embeddings of high-dimensional inputs. Based on the thought of LLE, we propose a novel unsupervised dimensionality reduction model called Local Linear Embedding with Adaptive Neighbors (LLEAN). To achieve a desirable dimensionality reduction result, we impose adaptive neighbor strategy and adopt a projection matrix to project data into an optimal subspace. The relationship between every pair-wise data is investigated to help reveal the data structure. Augmented Lagrangian Multiplier (ALM) is devised in optimization procedure to effectively solve the proposed objective function. Comprehensive experiments on toy data and benchmark datasets have been done and the results show that LLEAN outperforms other state-of-the-art dimensionality reduction methods.

Data-Driven Molecular Hallmarks of Acute Myeloid Leukemia: Biological, Prognostic and Therapeutic Implications

The cancer hallmark concept defines the biological processes that contribute to cancer development and progression. While the hallmarks represent an important theoretical framework, they reflect generic properties of all cancer types and hence most hallmarks are not measurable nor applicable to guiding diagnosis or therapy. Here, we carried out deep multi-omic profiling (DNA panel sequencing, , transcriptome, DNA methylation and proteome) and functional testing of responses to 525 drugs in 118 primary acute myeloid leukemia (AML). In addition, 47 primary AML patients were used as an independent validation cohort, including measurements by single-cell assays. Using unsupervised multi-omic dimensionality reduction, we defined 11 independent axes of biological variability that we considered here to reflect the data-driven hallmarks (DDHM) of AML. We defined the molecular features that contributed most to each hallmark, hence forming an understanding of the biological nature and pathways of each DDHM We hypothesized that the 11 DDHMs define biologically and clinically important features of AML applicable to diagnostics, therapy and precision medicine. For example, each patient had a unique composition of the DDHMs that reflected the AML biology of that patient case. Each DDHM was associated with distinct AML biology, including NPM1 mutation, transcriptional deregulation, stem cell properties, cell cycle, stromal signals, HOX signaling, and stress response. The DDHMs captured both biology that is already well known in AML, but with multi-omic resolution, and highlighted also novel, previously poorly understood, properties of AML. Each DDHM included a ranked list of drugs that explained the biology reflected in the hallmarks. For instance, the DDHM2 that defined sensitivity to Bcl-2 (Venetoclax) and MEK inhibitors highlighted myeloid cell differentiation patterns. Moreover, DDHM8 was strongly associated with patient prognosis, a higher response to hypomethylating agents and complete remission,. Combining this prognostic DDHM8 with hallmark DDHM1 that explained the omics of the NPM1 mutation landscape, captured the ELN risk classification and other prognostic cell surface markers. This revealed that several previously independently defined biomarkers of prognosis converge in the multi-omic paradigms defined by our DDHMs. In summary, we have defined through multi-omics and functional analysis a set of eleven data-driven hallmarks of AML. These capture some of the most important known prognostic and therapeutic biomarkers in AML but also highlight opportunities for novel biomarker discovery and drug repurposing. This approach provides a new quantitative and measurable framework to understand the biological heterogeneity of AML and exploit it for diagnosis, prognosis and precision therapy.

Reducing the dimensionality of data using tempered distributions

We reformulate unsupervised dimension reduction problem (UDR) in the language of tempered distributions, i.e. as a problem of approximating an empirical probability density function by another tempered distribution, supported in a k-dimensional subspace. We show that this task is connected with another classical problem of data science — the sufficient dimension reduction problem (SDR). In fact, an algorithm for the first problem induces an algorithm for the second and vice versa.In order to reduce an optimization problem over distributions to an optimization problem over ordinary functions we introduce a nonnegative penalty function that “forces” the support of the model distribution to be k-dimensional. Then we present an algorithm for the minimization of the penalized objective, based on the infinite-dimensional low-rank optimization, which we call the alternating scheme. Also, we design an efficient approximate algorithm for a special case of the problem, where the distance between the empirical distribution and the model distribution is measured by Maximum Mean Discrepancy defined by a Mercer kernel of a certain type. We test our methods on four examples (three UDR and one SDR) using synthetic data and standard datasets.

Unsupervised Adaptive Embedding for Dimensionality Reduction.

High-dimensional data are highly correlative and redundant, making it difficult to explore and analyze. Amount of unsupervised dimensionality reduction (DR) methods has been proposed, in which constructing a neighborhood graph is the primary step of DR methods. However, there exist two problems: 1) the construction of graph is usually separate from the selection of projection direction and 2) the original data are inevitably noisy. In this article, we propose an unsupervised adaptive embedding (UAE) method for DR to solve these challenges, which is a linear graph-embedding method. First, an adaptive allocation method of neighbors is proposed to construct the affinity graph. Second, the construction of affinity graph and calculation of projection matrix are integrated together. It considers the local relationship between samples and global characteristic of high-dimensional data, in which the cleaned data matrix is originally proposed to remove noise in subspace. The relationship between our method and local preserving projections (LPPs) is also explored. Finally, an alternative iteration optimization algorithm is derived to solve our model, the convergence and computational complexity of which are also analyzed. Comprehensive experiments on synthetic and benchmark datasets illustrate the superiority of our method.

Cyber-Attack Detection Using Principal Component Analysis and Noisy Clustering Algorithms: A Collaborative Machine Learning-Based Framework

This paper proposes a collaborative machine learning-based framework to detect cyber-attacks in a power system, leading to deviation in the state variable behavior. Based on the proposed architecture, three different machine learning-based methods, i.e., visualization, classification, and clustering, are employed and compared to find the best one in the FDIA detection process. To this end, pre-processing is employed in the first stage. In the second stage, the patterns of the state vectors are transferred into features. Hence, 24 statistical features, including measures of central tendency, variability, measures of shape, and position, are extracted to find various properties. Then, in the third stage, a supervised algorithm is employed to rank and find the most crucial features in FDIA. In the fourth stage, an unsupervised dimensionality reduction technique (PCA) is applied to reduce the feature space. In the fifth and last stage, visualization, classification, and clustering-based methods are developed to detect FDIA. To simulate an attack, it is assumed that an intruder decreases or increases the state vectors at different buses with various attack parameters (i.e., 0.90, 0.95, 0.96, 0.97, 0.98, 1, 1.02, 1.03, 1.04, 1.05, and 1.10). The proposed method effectiveness is assessed on the New York Independent System Operator (NYISO) data applied to the IEEE 14-bus system. The results presented in the paper from different scenarios (i.e., phase angle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> ), voltage magnitude ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{m}$ </tex-math></inline-formula> ), measurements, and multiple attacks) on a real-world dataset demonstrate that the collaborative optimized PCA-Density-based machine learning technique can detect most of the attack samples with good performance scores (i.e., recall, precision, F1) and outperforms the other investigated methods. Moreover, it is general and adaptable enough to cover the situation where either the system characteristics or the attack behavior changes.

Investigation of in-liquid ordering mediated transformations in Al-Sc via ab initio molecular dynamics and unsupervised learning

Scandium is well known to produce grain refinement in Al-based alloys, and its potency is generally attributed to intermetallic ${\mathrm{Al}}_{3}\mathrm{Sc}$ formation within liquid phase. However, the influence of Sc atoms and ${\mathrm{Al}}_{3}\mathrm{Sc}$ on the local structure of the surrounding melt, and subsequent nucleation remains unclear. Towards that end, we have probed structural changes in three bulk compositions, i.e., $\mathrm{Al}\text{\ensuremath{-}}x\mathrm{Sc}$ $(x=0, 0.4, 1.0 \mathrm{at}.%)$, and near liquid-$\mathrm{Al}/{\mathrm{Al}}_{3}\mathrm{Sc}$ interfacial regions using ab initio molecular dynamics. In-liquid ordering was determined using unsupervised learning techniques, i.e., structural fingerprinting, dimensionality reduction, and cluster analysis. Sc atoms ordered the surrounding liquid Al atoms by forming Sc-centered polyhedrons, while liquid-$\mathrm{Al}/{\mathrm{Al}}_{3}\mathrm{Sc}$ interface manifested planar ordering that resembled ${{100}}_{fcc\ensuremath{-}\mathrm{Al}}$. Both structures were geometrically persistent but constitutionally transient, i.e., they exchanged Al atoms with the surrounding liquid. This behavior was rationalized on the basis of their mixed metallic and covalent bond character. At a lower temperature, ${{100}}_{fcc\ensuremath{-}\mathrm{Al}}$ interfacial planes heteroepitaxially nucleated equilibrium $fcc\text{\ensuremath{-}}\mathrm{Al}$, while Sc-centered polyhedrons sequentially formed metastable $hcp$- and $bcc\text{\ensuremath{-}}\mathrm{Al}$. Using our simulations and extant experimental reports, we postulate two transformation pathways during Al-Sc solidification: (i) $\mathrm{Sc}\ensuremath{-}centered\phantom{\rule{4pt}{0ex}}polyhedrons\ensuremath{\rightarrow}{\mathrm{Al}}_{3}\mathrm{Sc}\ensuremath{\rightarrow}liquid\ensuremath{-}Al/A{l}_{3}Sc\phantom{\rule{4pt}{0ex}}interfacial\phantom{\rule{4pt}{0ex}}ordering\ensuremath{\rightarrow}fcc\ensuremath{-}\mathrm{Al}$; and (ii) $\mathrm{Sc}\ensuremath{-}centered\phantom{\rule{4pt}{0ex}}polyhedrons\ensuremath{\rightarrow}hcp\ensuremath{-}\mathrm{Al}\ensuremath{\rightarrow}bcc\ensuremath{-}\mathrm{Al}\ensuremath{\rightarrow}fcc\ensuremath{-}\mathrm{Al}$. The two in-liquid ordered structures provide an atomistic basis for the potency of Sc element, and, also, serve as possible structural metrics for designing novel Al-based alloys.

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE.

Feature Clustering of Noisy Data and Application in the Currency Market

With the increase in high-dimensional data, researchers pay more attention to dimensionality reduction techniques because there are many noisy, redundant and irrelevant features in high-dimensional data. The existence of noisy features leads to decrease performance when analyzing high-dimensional data. Also, unsupervised dimensionality reduction techniques are widely used due to the lack of available labels. Feature clustering is an unsupervised dimensionality reduction technique to partition features into clusters in which features are strongly related. In addition, the Pearson correlation coefficient is widely used as a similarity tool for feature clustering. However, the Pearson correlation coefficient is easily influenced by outliers and noises, thus leading to misleading results. This paper focuses on the influence of dissimilarity measures on the clustering of noisy features. Heavy-tailed distributions are used for modeling data with outliers and noises. Therefore, we introduce a new dissimilarity measure based on a new dependence coefficient of heavy-tailed distributions. The performance of feature clustering using the proposed dissimilarity is evaluated using ARI and internal criteria on artificial and real currency market datasets. Experiment results have demonstrated the effectiveness of the proposed feature clustering method.

The DataScope: A Mixed-Initiative Architecture for Data Labeling

Machine learning promises many advantages, but achieving these promises requires methods that smooth the interaction between humans and machine learning. Machine learning systems require meticulous training on large, labeled datasets. Labeling data is a tedious expensive process, and many times requires complex human-judgment skills. Mixed-initiative designs divide the labor between the artificial agent and the human to make the task at hand more efficient and effective. This paper proposes a mixed-initiative method for efficient coding of video and image data in general and emotion data in particular. We integrate an unsupervised dimensionality reduction algorithm and the R Shiny platform to develop an interactive method that leverages human expertise to label the data more efficiently and effectively. The method, through the interactive web tool, allows the user to explore the data interactively, examine similarities and dissimilarities in the data, and label clusters of many images and video frames at once. The combination of the unsupervised learning algorithm and the R Shiny platform enables interactive exploration and annotation of high-dimensional, complicated data. This method can be used to annotate large data sets faster and can advance research in machine vision.

The Origin and Discrimination of High-Ti Magnetite in Magmatic-Hydrothermal Systems: Insight from Machine Learning Analysis

AbstractHigh-titanium (high-Ti, more than 1 wt % Ti) magnetite, commonly containing ilmenite exsolution, has long been attributed to an igneous origin and has been used as the most critical factor in previously developed discriminant diagrams. However, recent studies have shown that high-Ti magnetite can be present in high-temperature hydrothermal deposits, suggesting a probable hydrothermal origin. This also calls for reconsideration and necessary modification of the currently available discriminant diagrams. This high-Ti magnetite issue is particularly acute in iron oxide-apatite (IOA) deposits and raises controversy in the discussion of the origin of the high-Ti magnetite.With statistical analysis and machine learning techniques, this study applies two unsupervised dimensionality reduction methods—principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—on a compiled data set consisting of 876 laser ablation-inductively coupled plasma-mass spectrometry analyses of primary high-Ti magnetite from high-temperature ore-forming systems worldwide. Three models are built with different element combinations to identify magnetite of different origins. The models were further evaluated by the support vectors machine (SVM) and receiver operating characteristic (ROC) curves and proved to be able to describe the characteristics of trace element compositions of high-Ti magnetite of different origins. Our models suggest that Mg, Mn, Al, Ti, V, and Co from 59 analyzed trace elements show promising properties as effective discriminators, and on this basis, a new discrimination diagram of lg(Al) + lg(Ti) + lg(V) versus lg(Mn)/[lg(Co) + lg(Mg)] is developed for distinguishing high-Ti magnetite of igneous and hydrothermal origin. Our results also show that the high-Ti magnetite in the IOA deposits has chemical compositions similar to those of high-temperature hydrothermal deposits, including the iron oxide copper-gold and porphyry deposits, but significantly distinct from the igneous magnetite. Our study, hence, implies a magmatic-hydrothermal origin for the magnetite in IOA deposits.

Unsupervised double weighted graphs via good neighbours for dimension reduction of hyperspectral image

ABSTRACT As the major research in pattern recognition, unsupervised dimension reduction is a challenging problem because of no label information. Most unsupervised dimension reduction methods usually construct similarity graph by k-nearest neighbour to preserve local structure in the low-dimensional subspace. However, k-nearest neighbour is calculated by Euclidean distance, which is sensitive to noise and outliers. And only considering local structure will reduce the classification accuracy. In this paper, a new unsupervised dimension reduction method called unsupervised double-weighted graphs via good neighbours (uDWG-GN) is proposed. First, uDWG-GN proposes a local structure Low-Rank Representation to learn similarity matrix and then uses the similarity matrix to find good neighbours of each sample. Second, according to good neighbours of the sample, uDWG-GN considers similar and dissimilar relationship between samples and constructs double-weighted graphs. Finally, based on norm, uDWG-GN finds the optimal projection matrix by maximizing the distance of dissimilar samples and minimizing the distance of similar samples. Experimental results on three hyperspectral images demonstrate the superiority and effectiveness of our method compared with other dimension reduction methods.

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors-Dependencies and Novel Pitfalls.

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.

A prediction-focused approach to personality modeling

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people’s personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room for improved predictive accuracy. We propose a novel approach to modeling human personality that is based on the maximization of the model’s predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized a supervised machine learning technique for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five-dimensional personality summary, which we term the “Predictive Five” (PF), provides predictive performance that is better than the FFM on two independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. The approach described herein has the promise of eventually providing an interpretable, low-dimensional personality representation, which is also highly predictive of behavior.