Semi-supervised local Fisher discriminant analysis for dimensionality reduction

When only a small number of labeled samples are available, supervised dimensionality reduction methods tend to perform poorly due to 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 has an analytic form of the globally optimal solution and it can be computed based on eigendecompositions. Therefore, the proposed method is computationally reliable and efficient. We show the effectiveness of the proposed method through extensive simulations with benchmark data sets.

• Type: Journal
• Year: 2010
• Where: ML
• Authors: Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, Jun Sese
• Keywords: Dimensionality Reduction Method, Globally Optimal Solution, Machine Learning, ML 2010, Semi-supervised Dimensionality Reduction