Sparse Modeling Representative Selection Research Articles

Joint feature and instance selection using manifold data criteria: application to image classification

In many pattern recognition applications feature selection and instance selection can be used as two data preprocessing methods that aim at reducing the computational cost of the learning process. Moreover, in some cases, feature subset selection can improve the classification performance. Feature selection and instance selection can be interesting since the choice of features and instances greatly influence the performance of the learnt models as well as their training costs. In the past, unifying both problems was carried out by solving a global optimization problem using meta-heuristics. This paradigm not only does not exploit the manifold structure of data but can be computationally expensive. To the best of our knowledge, the joint use of sparse modeling representative and feature subset relevance have not been exploited by the joint feature and selection methods. In this paper, we target the joint feature and instance selection by adopting feature subset relevance and sparse modeling representative selection. More precisely, we propose three schemes for the joint feature and instance selection. The first is a wrapper technique while the two remaining ones are filter approaches. In the filter approaches, the search process adopts a genetic algorithm in which the evaluation is mainly given by a score that quantify the goodness of the features and instances. An efficient instance selection technique is used and integrated in the search process in order to adapt the instances to the candidate feature subset. We evaluate the performance of the proposed schemes using image classification where classifiers are the nearest neighbor classifier and support vector machine classifier. The study is conducted on five public image datasets. These experiments show the superiority of the proposed schemes over various baselines. The results confirm that the filter approaches leads to promising improvement on classification accuracy when both feature selection and instance selection are adopted.

Bidirectional long short-term memory networks and sparse hierarchical modeling for scalable educational learning of dance choreographies

Recently, several educational game platforms have been proposed in the literature for choreographic training. However, their main limitation is that they fail to provide a quantitative assessment framework of a performing choreography against a groundtruth one. In this paper, we address this issue by proposing a machine learning framework exploiting deep learning paradigms. In particular, we introduce a long short-term memory network with the main capability of analyzing 3D captured skeleton feature joints of a dancer into predefined choreographic postures. This pose identification procedure is capable of providing a detailed (fine) evaluation score of a performing dance. In addition, the paper proposes a choreographic summarization architecture based on sparse modeling representative selection (SMRS) in order to abstractly represent the performing choreography through a set of key choreographic primitives. We have modified the SMRS algorithm in a way to extract hierarchies of key representatives. Choreographic summarization provides an efficient tool for a coarse quantitative evaluation of a dance. Moreover, hierarchical representation scheme allows for a scalable assessment of a choreography. The serious game platform supports advanced visualization toolkits using Labanotation in order to deliver the performing sequence in a formal documentation.

TSTSS: A two-stage training subset selection framework for cross version defect prediction

Cross Version Defect Prediction (CVDP) is a practical scenario by training the classification model on the historical data of the prior version and then predicting the defect labels of modules in the current version. Unfortunately, the differences of data distribution across versions may hinder the effectiveness of the trained CVDP model. Thus, it is not trivial to select a suitable training subset from the prior version to promote the CVDP performance. In this paper, we propose a novel method, called Two-Stage Training Subset Selection (TSTSS), to address this challenging issue. In the first stage, TSTSS utilizes a sparse modeling representative selection method to select an initial module subset from the prior version which can well reconstruct the data of the prior version. In the second stage, TSTSS leverages a dissimilarity-based sparse subset selection method to further refine the selected module subset, which enables the selected modules to well represent the modules of the current version. Finally, we use a novel weighted extreme learning machine classifier to construct the CVDP model. We evaluate the CVDP performance of TSTSS on 50 cross-version pairs using 6 indicators. The experiments show that TSTSS can efficiently improve the CVDP performance compared with 11 baseline methods.

Instance Selection Using Nonlinear Sparse Modeling

Sparse modeling representative selection (SMRS) has been recently introduced for selecting the most relevant examples in data sets. SMRS exploits data self-representativeness coding in order to infer a coding matrix with block sparsity constraint. The relevance scores of samples are then derived from the estimated matrix of coefficients. Since SMRS is based on a linear model for data self-representation, it cannot always provide good relevant samples. Besides, most of its selected samples can be found in dense areas in input space. In this paper, we propose to overcome the SMRS method’s shortcomings that are related to the coding matrix estimation. We introduce two nonlinear data self-representativeness coding schemes that are based on Hilbert space and column generation. Experimental evaluation is carried out on summarizing a video movie and on summarizing training image data sets used for classification tasks. These experiments demonstrated that the proposed nonlinear methods can outperform state-of-the art selection methods.

Spatio-temporal summarization of dance choreographies

An important issue in performing dance analysis is the automatic extraction of its choreographic patterns, since these elements provide an abstract representation of the semantics of the dance and encode the overall dance storytelling. However, application of conventional video summarization algorithms on dance sequences cannot appropriately retrieve their choreographic patterns, since a dance is composed of an ordered set of sequential elements which are often repeated in time. Additionally, 3D geometry is lost using color information. For this reason, in this paper we propose a new dance summarization scheme of 3D motion captured data (in the form of skeleton joints coordinates) recorded using the Vicon motion capture system. The proposed key frame extraction method implements a hierarchical scheme that exploits spatio-temporal variations of dance features. Initially, global holistic descriptors are extracted to localize the key choreographic steps of a dance (coarse representation). Then, each segment is further decomposed into finer sub-segments to improve dance representativity (fine representation). The abstraction scheme exploits the concepts of a Sparse Modeling Representative Selection (SMRS) appropriately modified to enable spatio-temporal modelling of the dance sequences through a hierarchical decomposition algorithm. Our approach is evaluated on thirty folkloric dance sequences recorded at the Aristotle University of Thessaloniki under the framework of Terpsichore project representing five different choreographies and on publicly available datasets from Carnegie–Mellon University, which depict performances on theatrical kinesiology. Comparisons with other traditional video summarization methods indicate a clear superiority of the proposed hierarchical spatio-temporal decomposition scheme.

Kernel sparse modeling for prototype selection

Recently, a new method termed Sparse Modeling Representative Selection (SMRS) has been proposed for selecting the most relevant instances in datasets. SMRS is based on data self-representativeness in the sense that it estimates a coding matrix using a dictionary of samples set to the data themselves. Sample relevances are derived from the matrix of coefficients with a block sparsity constraint. Due to the use of a linear model for data self-representation, SMRS cannot always provide good relevant samples. Besides, most of the selected relevant samples by SMRS are in dense regions. In this paper, we propose to overcome the shortcomings of the SMRS method by deploying non-linear data self-representativeness through the use of two kinds of data projections: kernel trick and column generation. Qualitative evaluation is performed on summarizing two video movies. Quantitative evaluations are obtained by performing classification tasks on the summarized training image datasets where the objective is to compare the relevance of selected samples for a given classification task and for a given instance selection method. The conducted experiments showed that the proposed methods can outperform state-of-the art methods including the SMRS method.

Decremental Sparse Modeling Representative Selection for prototype selection

Selecting few representatives or examples that can efficiently and reliably describe a set of data has always been a challenging task in computer vision and pattern recognition. Recently, Sparse Modeling Representative Selection (SMRS) was proposed as a powerful filter method for selecting the most relevant examples/instances in subspaces. The selection is achieved by ranking the examples using the L2 norm of the associated row in a coding matrix. This coding matrix is computed using data self-representativeness (the dictionary is given by the examples themselves) adopting block sparsity regularization. In this paper, we propose a decremental Sparse Modeling Representative Selection (D-SMRS) in which the selection of the representatives is broken down into several nested processes. The key contribution is a new scheme of sparse modeling that proceeds by progressive coding and pruning. It proceeds by eliminating outlier and noisy samples in the first stages so that the final stage (coding and selection) is performed on clean data. Thus, the final instance selection will not be heavily affected by the presence of outliers and aberrant samples. The proposed method was qualitatively and quantitatively evaluated. The qualitative evaluation concerned image selection and video summarization. The quantitative evaluation was performed on six benchmark image datasets using several state-of-the art selection methods with four different classifiers: 1-Nearest Neighbor (NN), Nearest Subspace (NS), Sparse Representation Classifier (SRC), and Support Vector Machines (SVM). The outlier rejection ability of the proposed method is also studied on real images. In all cases the selection computed by our algorithm achieved or outperformed existing state-of-the-art results.

Semi-Supervised Image Meta-Filtering Using Relevance Feedback in Cultural Heritage Applications

An image filtering scheme for images of cultural interest is presented. The proposed methodology utilize a semi supervised approach for the creation of an appropriate distance learning metric, which is used for the filtering. User's feedback is involved only for a minor set of data, defined using OPTICS algorithm and sparse modeling representative selection. Such an approach facilitates the refinement of retrieval results, always under the scope of the user needs. The described methodology can be easily implemented for a variety of feature vectors and data sets.