Alignment Kernel Research Articles

Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns

BackgroundPhysical activity is a crucial aspect of health benefits in the public society. Although studies on the temporal physical activity patterns might lead to the protocol for efficient intervention/program, a standardized procedure to determine and analyze the temporal physical activity patterns remains to be developed. Here, we attempted to develop a procedure to cluster 24-hour patterns of physical activity as step counts measured with an accelerometer-based wearable sensor.MethodsThe 1 Hz step count data was collected by a hip-worn triaxial accelerometer from 42 healthy participants, comprising 35 males and 7 females, at the Sendai Oroshisho center in 2008. This is a cross sectional study using unsupervised machine learning, specifically the kernel k-means algorithm with the global alignment kernel was applied on a total of 815 days from 42 participants, and 6 activity patterns were identified. Further, the probability of each 24-hour step-counting pattern was calculated for every participant and used for spectral clustering of step-behavioral patterns.ResultsWe could identify six 24-hour step-counting patterns and five daily step-behavioral clusters. We could further identify five step-behavioral clusters, all-day dominant (21 participants), all-day + bi-phasic dominant (8 participants), bi-phasic dominant (6 participants), all-day + evening dominant (4 participants), and morning dominant (3 participants). When the amount of physical activity was categorized into tertile groups reflecting highly active, moderately active, and low active, each tertile group consisted of different proportions of six 24-hour step-counting patternsConclusionsOur study introduces a novel approach using an unsupervised machine learning method to categorize daily hourly activity, revealing six distinct step counting patterns and five clusters representing daily step behaviors. Our procedure would be reliable for finding and clustering physical activity patterns/behaviors and reveal diversity in the categorization by a traditional tertile procedure using total step amount.

Cross-Chamber Data Transferability Evaluation for Fault Detection and Classification in Semiconductor Manufacturing

Unit-to-unit variation among the production chambers is a long-lasting challenge for Fault Detection and Classification (FDC) development in the semiconductor industry. Currently, various methods are applied for knowledge transfer among chambers and generalized FDC model development. However, the existing methods cannot give a quantitative or qualitative measure for cross-chamber data transferability evaluation. This research proposes a novel methodology for data transferability evaluation and important sensor screening, which can serve as a data quality evaluation tool for any FDC model. In this research, firstly, Time Series Alignment Kernel (TSAK) is incorporated into Multidomain Discriminant Analysis (MDA) algorithm to achieve sensor-based domain generalization. Then, domain-invariant features are directly extracted for sensor visualization. After that, Fisher’s criterion ratios of the labeled good wafer samples and defective ones are computed based on the domain-invariant features of each sensor to quantitatively estimate how easy it is to transfer knowledge of each sensor among chambers, i.e., data transferability evaluation. Lastly, the proposed method develops a Recursive Feature Elimination (RFE)-based sensor selection algorithm to qualitatively analyze the importance of each sensor channel and identify the critical sensor subset. In this study, validation of the proposed method is based on two open-source datasets from real production lines.

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data. Based on the above improvements, we create a novel anomaly detection strategy FastICA-TGAK-OCELM. The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies (ALFA) dataset. The experimental results show that compared with other methods, the accuracy of this method is improved by more than 30%, and point anomalies are effectively detected.

Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis

Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-series domain due to its unique characteristics compared to images and text data. In this paper, we fill this gap by proposing a novel algorithmic framework referred as RObust Training for Time-Series (RO-TS) to create robust deep models for time-series classification tasks. Specifically, we formulate a min-max optimization problem over the model parameters by explicitly reasoning about the robustness criteria in terms of additive perturbations to time-series inputs measured by the global alignment kernel (GAK) based distance. We also show the generality and advantages of our formulation using the summation structure over time-series alignments by relating both GAK and dynamic time warping (DTW). This problem is an instance of a family of compositional min-max optimization problems, which are challenging and open with unclear theoretical guarantee. We propose a principled stochastic compositional alternating gradient descent ascent (SCAGDA) algorithm for this family of optimization problems. Unlike traditional methods for time-series that require approximate computation of distance measures, SCAGDA approximates the GAK based distance on-the-fly using a moving average approach. We theoretically analyze the convergence rate of SCAGDA and provide strong theoretical support for the estimation of GAK based distance. Our experiments on real-world benchmarks demonstrate that RO-TS creates more robust deep models when compared to adversarial training using prior methods that rely on data augmentation or new definitions of loss functions. We also demonstrate the importance of GAK for time-series data over the Euclidean distance.

Phenotyping metabolic status of dairy cows using clustering of time profiles of energy balance peripartum

Due to a combination of a relatively low energy intake and a high demand of energy required for milk production, dairy cows experience a negative energy balance (EB) at the start of lactation. This energy deficit causes body weight reduction and an increased risk for metabolic diseases. Severity and length of negative EB can differ among cows. Peripartum time profiles of EB for dairy cows are not described yet in the literature. Creating EB-derived time profiles with corresponding metabolic status and disease treatments could improve understanding the relationship between EB and metabolic status, as well as enhance identification of cows at risk for compromised metabolic status. In this research we propose a novel method to cluster EB time series and examine associated metabolic status and disease treatments of dairy cows in the peripartum period. In this study, data of 3 earlier experiments were merged and examined. Four dairy cow clusters for time profiles of EB from wk -3 until +7 relative to calving were generated by the global alignment kernel algorithm. For each cluster, mean of body weight prepartum was distinguishable, indicating this might be a possible on-farm biomarker for the peripartum EB profile. Moreover, cows with severe EB drop postpartum were more treated for milk fever and had high plasma nonesterified fatty acids and β-hydroxybutyrate concentration, and low IGF-1, insulin, and glucose concentration in the first 7 wk of lactation. Overall, this study demonstrated that cows can be clustered based on EB time profiles and that characteristics such as prepartum body weight, and postpartum nonesterified fatty acids and glucose concentration are promising biomarkers to identify the time profile of EB and potentially the risk for metabolic diseases.

Time-Series Clustering Benchmark on Regional Economic Indicator

This paper presents a benchmark study on time-series clustering using regional economic data from the World Bank Open Data (WBOD) repository. It serves as a paradigm for future researchers. This study compares the effectiveness of twenty different techniques for time series grouping. They combine three clustering algorithms (partitional, hierarchical, and fuzzy), two centroids (K-means and K-medoids), and four distance measurements (distance between two points on a graph) (Dynamic time warping, Euclidean, shape-based distance, and global triagonal kernel alignment). The internal clustering validation index will be used to compare the performance of various techniques. Additionally, statistical tests are run on the performance of the pair of approaches to establish whether they can be compared. Across all clustering algorithms evaluated, it was discovered that utilizing K-means as centroids outperformed using K-medoids. When it comes to distance measurements, all clustering algorithms perform optimally, but the Triagonal Global Alignment Kernel is the best of these (except for the fuzzy C-means). Another conclusion reached in this study is that no solution utilizing Dynamic Time Warping and Euclidean distance measures can be compared to another (insignificant Wilcoxon test result). Simultaneously, Shape-Based Distance consistently beats all other approaches to clustering in terms of consistency.

A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

Rapid and accurate crop type mapping is of great significance for agricultural management and sustainable development. Time-series multi-polarization synthetic aperture radar (SAR) data is suitable for obtaining the large-scale distribution of crop types and continuously monitoring crops. At present, the classification method based on the time-series alignment of time-varying feature curves has been widely used, which can take the uncertainty of the phenological cycles of crops into account. The most classical method is the nearest neighbor (NN) classifier based on the dynamic time warping (DTW) alignment. While the DTW alignment does not consider the local shape of the curves and temporal ranges, and the NN classifier is inadequate in generalization, which restricts the accuracy of crop type mapping. In this paper, a pairwise proximity function support vector machine (ppfSVM) classification method with the time-weighted shapeDTW (TWshapeDTW) alignment is proposed. Firstly, the novel alignment method simultaneously considers the local shape of the curve and the temporal range of crops. Besides, the novel ppfSVM classifier with the time-series alignment kernel is established. Such kernel matrix considers dual-similarity metrics of multiple features, and it is positive semi-definite (PSD) in this classifier. With 42 Sentinel-1 dual-polarization SAR images located in Gansu Province, China, the crop classification maps in 2018 and 2019 are generated respectively. The proposed method in this paper obtains the overall accuracies of more than 90% in both two years, and the changes of crop types from 2018 to 2019 are also in line with the actual crop rotation. Compared with traditional classification methods (SVM and the NN method with the DTW alignment), it is found that the proposed method has higher overall accuracy (OA) and better robustness in the case of small number of samples. The OA's improvements of our method compared with the SVM method are 3% and 1% in 2018 and 2019, respectively. Such improvements are 14% and 12% respect to the NN method with the DTW alignment. This method can achieve more obvious improvement under the condition of less training samples and unaligned phenological sequences. Furthermore, the novel TWshapeDTW alignment is superior to the DTW and the time-weighted DTW alignment under the ppfSVM classifier. The OA's improvement introduced by the TWshapeDTW alignment respect to the DTW alignment can obtain 5% and 4% in 2018 and 2019, respectively.

Satellite data and machine learning reveal the incidence of late frost defoliations on Iberian beech forests.

Climate warming is driving an advance of leaf unfolding date in temperate deciduous forests, promoting longer growing seasons and higher carbon gains. However, an earlier leaf phenology also increases the risk of late frost defoliation (LFD) events. Compiling the spatiotemporal patterns of defoliations caused by spring frost events is critical to unveil whether the balance between the current advance in leaf unfolding dates and the frequency of LFD occurrence is changing and represents a threaten for the future viability and persistence of deciduous forests. We combined satellite imagery with machine learning techniques to reconstruct the spatiotemporal patterns of LFD events for the 2003-2018 period in the Iberian range of European beech (Fagus sylvatica), at the drier distribution edge of the species. We used MODIS Vegetation Index Products to generate a Normalized Difference Vegetation Index (NDVI) time series for each 250 × 250m pixel in a total area of 1,013 km2 (16,218 pixels). A semi-supervised approach was used to train a machine learning model, in which a binary classifier called Support Vector Machine with Global Alignment Kernel was used to differentiate between late frost and non-late frost pixels. We verified the obtained estimates with photointerpretation and existing beech tree-ring chronologies to iteratively improve the model. Then, we used the model output to identify topographical and climatic factors that determined the spatial incidence of LFD. During the study period, LFD was a low recurrence phenomenon that occurred every 15.2yr on average and showed high spatiotemporal heterogeneity. Most LFD events were condensed in 5 yr and clustered in western forests (86.5% in one-fifth of the pixels) located at high elevation with lower than average precipitation. Elevation and longitude were the major LFD risk factors, followed by annual precipitation. The synergistic effects of increasing drought intensity and rising temperature combined with more frequent late frost events may determine the future performance and distribution of beech forests. This interaction might be critical at the beech drier range edge, where the concentration of LFD at high elevations could constrain beech altitudinal shifts and/or favor species with higher resistance to late frosts.

Touch Modality Identification With Tensorial Tactile Signals: A Kernel-Based Approach

Touch modality identification has attracted increasing attention due to its importance in human&#x2013;robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods. <i>Note to Practitioners</i>&#x2014;In a wealth of tactile recognition tasks, we are in the face of various challenges. For instance, tactile measurements are commonly tensorial and high-dimensional. The misalignments among tactile measurements prevail, such as different durations of tactile measurements and the misaligned starting time point of tactile measurements. This article presents a kernel-based method using an ideal regularized composite kernel to deal with all challenges in a unified framework. The kernel-based method consists of two key components including the SVD-based kernel and the global alignment kernel. The proposed method may shed new insights on new advances in tactile signal processing particularly in human&#x2013;robot interactions.

Methodology for Important Sensor Screening for Fault Detection and Classification in Semiconductor Manufacturing

Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful sensor channels for FDC model development in semiconductor manufacturing. The proposed method can be used as a pre-processing step prior to feature extraction, and the selected sensor channels can be leveraged by process engineers for finer feature design. In this research, firstly, time series alignment kernels (TSAKs) are proposed to handle multivariate trace data. Then, the proposed method combines 5 different time series alignment kernels (TSAKs) with a feature selection algorithm, minimum Redundancy Maximum Relevance (mRMR), to identify the important sensor channels. Furthermore, a TSAK+Kernel Principal Component Analysis (KPCA) algorithm is proposed for a visualization tool. Lastly, the TSAK+Support Vector Machine (SVM) is employed for results validation. In this study, validation of the proposed method is based on both open-source datasets and the proprietary datasets from a real production line.

Intelligent fault diagnosis of high-voltage circuit breakers using triangular global alignment kernel extreme learning machine

In recent years, vibration-based intelligent fault diagnosis of high-voltage circuit breakers (HVCBs) exhibits excellent performance. It requires a reliable machine learning method to develop an automatically diagnostic model to recognize the mechanical state from vibration signals. However, the traditional machine learning methods tend to produce unstable diagnostic results under the case of sampling asynchrony caused by the fluctuation of the control voltage. To address this problem, an improved kernel extreme learning machine (K-ELM) called triangular global alignment kernel (TGAK) extreme learning machine (TGAK-ELM) was presented in this study, which was developed by introducing TGAK into K-ELM. The TGAK is an elastic kernel which was designed by considering all the possible alignments between samples. Therefore, it provides a flexible similarity measure for samples, resulting in the improvement of the diagnostic performance. Experiments on the 35kV HVCB verified the effectiveness of the proposed method. Compared to other state-of-the-art machine learning methods, the proposed TGAK-ELM produced better diagnostic results. And further experiments on eight datasets picked from UCR repository suggested the applicability of TGAK-ELM in other fields.

Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics

An epidemic caused by COVID-19 in China turned into pandemic within a short duration affecting countries worldwide. Researchers and companies around the world are working on all the possible strategies to develop a curative or preventive strategy for the same, which includes vaccine development, drug repurposing, plasma therapy, and drug discovery based on Artificial intelligence. Therapeutic approaches based on Computational biology and Machine-learning algorithms are specially considered, with a view that these could provide a fast and accurate outcome in the present scenario. As an effort towards developing possible therapeutics for COVID-19, we have used machine-learning algorithms for the generation of alignment kernels from diverse viral sequences of Covid-19 reported from India, China, Italy and USA. Using these diverse sequences we have identified the conserved motifs and subsequently a peptide library was designed against them. Of these, 4 peptides have shown strong binding affinity against the main protease of SARS-CoV-2 (Mpro) and also maintained their stability and specificity under physiological conditions as observed through MD Simulations. Our data suggest that these evolutionary peptides against COVID-19 if found effective may provide cross-protection against diverse Covid-19 variants.

WiPOS: A POS Terminal Password Inference System Based on Wireless Signals

WiFi access points are sources of considerable security risks as the wireless signals have the potential to leak important private information such as passwords. This article examines the security issues posed by point-of-sale (POS) terminals which are widely used in WiFi-covered environments, such as restaurants, banks, and libraries. In particular, we envisage an attack model on passwords entered on POS terminals. We put forward the WiPOS, a password inference system based on wireless signals. Specifically, the WiPOS is a device-free system that uses two commercial off-the-shelf (COTS) devices to collect WiFi signals. Implementing a new keystroke segmentation algorithm and adopting support vector machine (SVM) classifiers with global alignment kernel (GAK), the WiPOS achieves improvement on both keystroke recognition and password prediction. The experimental results show that the WiPOS can achieve more than 73% accuracy for 6-digit password with the top 100 candidates. This article calls the community to take a closer look at the risks posed by the current ubiquitous WiFi devices.

Flocking Hydrodynamics with External Potentials

We study the large-time behavior of hydrodynamic model which describes the collective behavior of continuum of agents, driven by pairwise alignment interactions with additional external potential forcing. The external force tends to compete with alignment which makes the large time behavior very different from the original Cucker-Smale (CS) alignment model, and far more interesting. Here we focus on uniformly convex potentials. In the particular case of \emph{quadratic} potentials, we are able to treat a large class of admissible interaction kernels, $\phi(r) \gtrsim (1+r^2)^{-\beta}$ with `thin' tails $\beta \leq 1$ --- thinner than the usual `fat-tail' kernels encountered in CS flocking $\beta\leq\frac{1}{2}$: we discover unconditional flocking with exponential convergence of velocities \emph{and} positions towards a Dirac mass traveling as harmonic oscillator. For general convex potentials, we impose a stability condition, requiring large enough alignment kernel to avoid crowd scattering. We then prove, by hypocoercivity arguments, that both the velocities \emph{and} positions of smooth solution must flock. We also prove the existence of global smooth solutions for one and two space dimensions, subject to critical thresholds in initial configuration space. It is interesting to observe that global smoothness can be guaranteed for sub-critical initial data, independently of the apriori knowledge of large time flocking behavior.

Space-Efficient Feature Maps for String Alignment Kernels

String kernels are attractive data analysis tools for analyzing string data.Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit-sensitive parsing and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses.The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications.We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.

Analyzing Group-Level Emotion with Global Alignment Kernel based Approach

From the perspective of social science, understanding group emotion has become increasingly important for teams to considerably accomplish organizational work. Currently, automatically analyzing the perceived affect of a group of people has been received increasingly interest in affective computing community. The variability in group size makes difficulty for group-level emotion recognition to straightforwardly measure the feature distance of two group-level images. Recent works attempted to resolve the preceding problem by using feature encoding. However, the early works lack of efficiency. To alleviate this problem, this article aims to design a new method to effectively analyze the group behavior from a group-level image. Motivated by time-series kernel approaches explored in dynamic facial expression classification, this article mainly concentrates on global alignment kernel and design support vector machine with the combined global alignment kernels (SVM-CGAK) to better recognize group-level emotion. Specifically, we first propose to use global alignment kernel to explicitly measure the distance of two group-level images. For improving the performance of global alignment kernel, we use the global weight sort scheme based on their spatial relation information to sort the faces from group-level image, making an efficient data structure to the global alignment kernel. With this new global alignment kernel, we construct the backbone of SVM-CGAK, namely, support vector machine with global alignment kernel. Furthermore, considering the challenging environment, we construct two global alignment kernels based on Reisz-based Volume Local Binary Pattern and deep convolutional neural network features, respectively. Lastly, to make the robustness of group-level emotion recognition, we propose SVM-CGAK combining both global alignment kernels with multiple kernel learning approach. It can enhance the discriminative ability of each global alignment kernel. Intensive experiments are conducted on three challenging group-level emotion databases. The experimental results demonstrate that the proposed approach achieves promising performance for group-level emotion recognition compared with the recent state-of-the-art methods.

Dynamic time alignment kernel-based fuzzy clustering of non-equal length vector time series

Time series clustering is an effective vehicle to explore and visualize the structure of a suite of time series. In this study, we generalize the kernel-based fuzzy c-means clustering algorithm by involving the dynamic time alignment kernel (DTAK) to cluster vector time series. In this method, the nonlinear time alignment embedded in DTAK makes the kernel-based fuzzy c-means available for sequences with variable lengths. However, it is noted that DTAK is not a strictly positive definite kernel, especially when the sample size is large. To overcome this, some strategies are presented to make the proposed algorithm available for large data sets. In addition, it is a challenge task to calculate the average sequence for a series of time series with different lengths. In kernel-based fuzzy c-means algorithm, it is not necessary to calculate the average sequence, which will increase the effectiveness of clustering techniques for time series. In the experiments, the kernel-based fuzzy c-means with DTAK is evaluated by both the data sets from the UCI KDD Archive and real-world data sets. Experimental results delivered by the proposed method demonstrate its effectiveness and robustness.

GPU accelerated sequence alignment with traceback for GATK HaplotypeCaller

BackgroundPairwise sequence alignment is widely used in many biological tools and applications. Existing GPU accelerated implementations mainly focus on calculating optimal alignment score and omit identifying the optimal alignment itself. In GATK HaplotypeCaller (HC), the semi-global pairwise sequence alignment with traceback has so far been difficult to accelerate effectively on GPUs.ResultsWe first analyze the characteristics of the semi-global alignment with traceback in GATK HC and then propose a new algorithm that allows for retrieving the optimal alignment efficiently on GPUs. For the first stage, we choose intra-task parallelization model to calculate the position of the optimal alignment score and the backtracking matrix. Moreover, in the first stage, our GPU implementation also records the length of consecutive matches/mismatches in addition to lengths of consecutive insertions and deletions as in the CPU-based implementation. This helps efficiently retrieve the backtracking matrix to obtain the optimal alignment in the second stage.ConclusionsExperimental results show that our alignment kernel with traceback is up to 80x and 14.14x faster than its CPU counterpart with synthetic datasets and real datasets, respectively. When integrated into GATK HC (alongside a GPU accelerated pair-HMMs forward kernel), the overall acceleration is 2.3x faster than the baseline GATK HC implementation, and 1.34x faster than the GATK HC implementation with the integrated GPU-based pair-HMMs forward algorithm. Although the methods proposed in this paper is to improve the performance of GATK HC, they can also be used in other pairwise alignments and applications.

S3DRGF: Spatial 3-D Relational Geometric Features for 3-D Sign Language Representation and Recognition

Locations, angles, edges, and surfaces are spatial joint features that were predominantly used for characterizing three-dimensional (3-D) skeletal data in human action recognition. Despite their demonstrated success, features described earlier find difficulty in representing a relational change among joint movements in 3-D space for classifying human actions. To characterize a relation between joints on 3-D skeleton, we propose spatial 3-D relational geometric features (S3DRGFs). S3DRGFs are calculated on a subset of four joints in a chronological order covering all joints on the skeleton. Each of these four joints shape into a polygon, that reshapes spatially and temporally with respect to the sign (action) in the 3-D video. Consequently, we construct the spatio-temporal features (S3DRGF) by computing the area and perimeter of these polygons. Accordingly, query 3-D sign (action) recognition process transforms the joint area and perimeter features (JAF and JPF) into global alignment kernels based on the computed similarity scores with the dataset features. The similarity scores from JAF and JPF kernels are averaged for recognition. The proposed framework has been tested on our own 3-D sign language dataset (BVC3DSL) and three other publicly available datasets: HDM05, CMU, and NTU RGBD skeletal data. The results show higher levels of accuracy in decoding 3-D sign language into text for building a 3-D model based sign language translator.

Local-global nested graph kernels using nested complexity traces

In this paper, we propose two novel local-global nested graph kernels, namely the nested aligned kernel and the nested reproducing kernel, drawing on depth-based complexity traces. Both of the nested kernels gauge the nested depth complexity trace through a family of K-layer expansion subgraphs rooted at the centroid vertex, i.e., the vertex with minimum shortest path length variance to the remaining vertices. Specifically, for a pair of graphs, we commence by computing the centroid depth-based complexity traces rooted at the centroid vertices. The first nested kernel is defined by measuring the global alignment kernel, which is based on the dynamic time warping framework, between the complexity traces. Since the required global alignment kernel incorporates the whole spectrum of alignment costs between the complexity traces, this nested kernel can provide rich statistic measures. The second nested kernel, on the other hand, is defined by measuring the basic reproducing kernel between the complexity traces. Since the associated reproducing kernel only requires time complexity O(1), this nested kernel has very low computational complexity. We theoretically show that both of the proposed nested kernels can simultaneously reflect the local and global graph characteristics in terms of the nested complexity traces. Experiments on standard graph datasets abstracted from bioinformatics and computer vision databases demonstrate the effectiveness and efficiency of the proposed graph kernels.