Sparse Discrimination based Multiset Canonical Correlation Analysis for Multi-Feature Fusion and Recognition

Hongkun Ji, Xiaobo Shen, Quansen Sun and Zexuan Ji

Abstract

Multiset canonical correlation analysis is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic sparse reconstructive relationship and discriminating structure of multiple data spaces in real-world applications. In this paper, by taking discriminative information of within-class and between-class sparse reconstruction into account, we propose a novel algorithm, called sparse discrimination based multiset canonical correlations (SDbMCCs), to explicitly consider both discriminative structure and sparse reconstructive relationship in multiple representation data. In addition to maximizing between-set cumulative correlations, SDbMCC minimizes within-class sparse reconstructive distances and maximizes between-class sparse reconstructive distances, simultaneously. The feasibility and effectiveness of the proposed method is verified on four popular databases (CMU PIE, ETH-80, AR and Extended Yale-B) with promising results.

Session

Poster 2

Files

Extended Abstract (PDF, 32K)
Paper (PDF, 643K)

DOI

10.5244/C.29.141
https://dx.doi.org/10.5244/C.29.141

Citation

Bibtex

@inproceedings{BMVC2015_141,
	title={Sparse Discrimination based Multiset Canonical Correlation Analysis for Multi-Feature Fusion and Recognition},
	author={Hongkun Ji and Xiaobo Shen and Quansen Sun and Zexuan Ji},
	year={2015},
	month={September},
	pages={141.1-141.9},
	articleno={141},
	numpages={9},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.141},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.141}
}