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Binary- and Multi-class Group Sparse Canonical Correlation Analysis for Feature Extraction and Classification
Oct. 2013 (vol. 25 no. 10)
pp. 2192-2205
Zhao Zhang, City University of Hong Kong, Hong Kong
Mingbo Zhao, City University of Hong Kong, Hong Kong
Tommy W.S. Chow, City University of Hong Kong, Hong Kong
This paper incorporates the group sparse representation into the well-known canonical correlation analysis (CCA) framework and proposes a novel discriminant feature extraction technique named group sparse canonical correlation analysis (GSCCA). GSCCA uses two sets of variables and aims at preserving the group sparse (GS) characteristics of data within each set in addition to maximize the global interset covariance. With GS weights computed prior to feature extraction, the locality, sparsity and discriminant information of data can be adaptively determined. The GS weights are obtained from an NP-hard group-sparsity promoting problem that considers all highly correlated data within a group. By defining one of the two variable sets as the class label matrix, GSCCA is effectively extended to multiclass scenarios. Then GSCCA is theoretically formulated as a least-squares problem as CCA does. Comparative analysis between this work and the related studies demonstrate that our algorithm is more general exhibiting attractive properties. The projection matrix of GSCCA is analytically solved by applying eigen-decomposition and trace ratio (TR) optimization. Extensive benchmark simulations are conducted to examine GSCCA. Results show that our approach delivers promising results, compared with other related algorithms.
Index Terms:
Vectors,Correlation,Feature extraction,Sparse matrices,Optimization,Iron,Encoding,multiclass classification,Vectors,Correlation,Feature extraction,Sparse matrices,Optimization,Iron,Encoding,feature extraction,Canonical correlation analysis,group sparse representation
Zhao Zhang, Mingbo Zhao, Tommy W.S. Chow, "Binary- and Multi-class Group Sparse Canonical Correlation Analysis for Feature Extraction and Classification," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2192-2205, Oct. 2013, doi:10.1109/TKDE.2012.217
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