Quality-based Multimodal Classification Using Tree-Structured Sparsity

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classification. Among several sparsity models, tree-structured sparsity provides a flexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. To demonstrate their efficacy, the proposed methods are evaluated on three different applications - multiview face recognition, multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014

Time series clustering for fault detection and isolation

Fault detection and isolation (FDI) algorithms have been widely studied in recent years. Most of the existing algorithms are supervised. The modified Gath-Geva (MGG) algorithm has been recently introduced as an unsupervised method for time series segmentation and condition monitoring. This algorithm is applied here for FDI purpose on DAMADICS benchmark. However, it fails to classify the faults properly because of the high dimensionality of the data. To tackle this problem, dynamic PCA (DPCA) is then utilized as a preprocessing step to enhance the informative richness of the data set and to reduce the dimension of the data. The derived DPCA-MGG clustering approach is used to detect and isolate the faults by organizing the DPCA-transformed data in different clusters. This method results in better isolation of the faults than the original MGG algorithm. Another methodology to overcome the high dimensionality problem is feature weighting which is also incorporated here to enhance the monitoring task. For this purpose, a new clustering method is introduced here which provides weights for different features in different clusters through an optimization procedure. This method can properly detect and isolate the faults of the DAMADICS benchmark while outperforming the other discussed methods