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Self-enhanced relevant component analysis with side-information and unlabeled data

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3 Author(s)
Fei Wu ; Dept. of Autom., Tsinghua Univ., Beijing, China ; Yonglei Zhou ; Changshui Zhang

Relevant component analysis (RCA) is a powerful tool for relevant linear feature extraction with side-information, a new focus in machine learning fields. But its only utilizing positive constraints weakens this algorithm's performance and robustness, especially when there are few positive constraints - a common case in practice. To overcome this drawback, in this paper we propose an extended algorithm named self-enhanced relevant component analysis (SERCA). Through a boosting procedure in the product space, it efficiently uses both the given side-information and unlabeled data. The experimental results on several data sets show that SERCA achieves an obvious improvement compared with RCA.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:2 )

Date of Conference:

25-29 July 2004