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Pattern Recognition, International Conference on (2004)
Cambridge UK
Aug. 23, 2004 to Aug. 26, 2004
ISSN: 1051-4651
ISBN: 0-7695-2128-2
pp: 582-585
Fei Wu , Tsinghua University, Beijing, China
Yonglei Zhou , Tsinghua University, Beijing, China
Changshui Zhang , Tsinghua University, Beijing, China
ABSTRACT
"Learning with side-information" is attracting more and more attention in machine learning problems. In this paper, we propose a general iterative framework for relevant linear feature extraction. It efficiently utilizes both the side-information and unlabeled data to enhance gradually algorithms' performance and robustness. Both good relevant feature extraction and reasonable similarity matrix estima-tion can be realized. Specifically, we adopt Relevant Component Analysis (RCA) under this framework and get the derived Iterative Self-Enhanced Relevant Component Analysis (ISERCA) algorithm. The experimental results on several data sets show that ISERCA outperforms RCA.
INDEX TERMS
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CITATION

F. Wu, Y. Zhou and C. Zhang, "Relevant Linear Feature Extraction Using Side-information and Unlabeled Data," Pattern Recognition, International Conference on(ICPR), Cambridge UK, 2004, pp. 582-585.
doi:10.1109/ICPR.2004.1334596
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