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