Semi-supervised local Fisher discriminant analysis for dimensionality reduction

Authors:
Masashi Sugiyama;Tsuyoshi Idé;Shinichi Nakajima;Jun Sese
Affiliations:
Department of Computer Science, Tokyo Institute of Technology, Meguro-ku, Japan 152-8552;IBM Research, Tokyo Research Laboratory, Yamato-shi, Japan 242-8502;Nikon Corporation, Kumagaya-shi, Japan 360-8559;Department of Information Science, Ochanomizu University, Bunkyo, Japan 112-8610
Venue:
Machine Learning
Year:
2010

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Abstract

When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly because of overfitting. In such cases, unlabeled samples could be useful in improving the performance. In this paper, we propose a semi-supervised dimensionality reduction method which preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The proposed method, which we call SEmi-supervised Local Fisher discriminant analysis (SELF), has an analytic form of the globally optimal solution and it can be computed based on eigen-decomposition. We show the usefulness of SELF through experiments with benchmark and real-world document classification datasets.