Discriminative canonical correlation analysis with missing samples

Sun, Tingkai, Chen, Songccan, Yang, Jingyu, Hu, Xuelei and Shi, Pengfei (2009). Discriminative canonical correlation analysis with missing samples. In: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering. 2009 WRI World Congress on Computer Science and Information Engineering (CSIE 2009), Los Angeles, United States, (95-99). 31 March 2009 - 2 April 2009. doi:10.1109/CSIE.2009.794

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Author Sun, Tingkai
Chen, Songccan
Yang, Jingyu
Hu, Xuelei
Shi, Pengfei
Title of paper Discriminative canonical correlation analysis with missing samples
Conference name 2009 WRI World Congress on Computer Science and Information Engineering (CSIE 2009)
Conference location Los Angeles, United States
Conference dates 31 March 2009 - 2 April 2009
Proceedings title Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering
Place of Publication Los Alamitos, CA, United States
Publisher IEEE
Publication Year 2009
Sub-type Fully published paper
DOI 10.1109/CSIE.2009.794
Open Access Status
ISBN 9780769535074
Start page 95
End page 99
Total pages 5
Language eng
Abstract/Summary Multimodal recognition emerges when the non-robustness of unimodal recognition is noticed in real applications. Canonical correlation analysis (CCA) is a powerful tool of feature fusion for multimodal recognition. However, in CCA, the samples must be pairwise, and this requirement may not easily be met due to various unexpected reasons. Additionally, the class information of the samples is not fully exploited in CCA. These disadvantages restrain CCA from extracting more discriminative features for recognition. To tackle these problems, in this paper, the class information is incorporated into the framework of CCA for recognition, and a novel method for multimodal recognition, called discriminative canonical correlation analysis with missing samples (DCCAM), is proposed. DCCAM can tolerate the missing of samples and need not artificially make up the missing samples so that its computation is timesaving and space-saving. The experimental results show that 1) DCCAM outperforms the related multimodal recognition methods; and 2) the recognition accuracy of DCCAM is relatively insensitive to the number of missing samples.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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Created: Mon, 16 Dec 2013, 12:49:40 EST by Xuelei Hu on behalf of School of Information Technol and Elec Engineering