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Semi-supervised classification based on random subspace dimensionality reduction
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DOI：10.1016/j.patcog.2011.08.024.png)
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Graph structure is vital to graph based semi-supervised learning. However, the problem of constructing a graph that reflects the underlying data distribution has been seldom investigated in semi-supervised learning, especially for high dimensional data. In this paper, we focus on graph construction for semi-supervised learning and propose a novel method called Semi-Supervised Classification based on Random Subspace Dimensionality Reduction, SSC-RSDR in short. Different from traditional methods that perform graph-based dimensionality reduction and classification in the original space, SSC-RSDR performs these tasks in subspaces. More specifically, SSC-RSDR generates several random subspaces of the original space and applies graph-based semi-supervised dimensionality reduction in these random subspaces. It then constructs graphs in these processed random subspaces and trains semi-supervised classifiers on the graphs. Finally, it combines the resulting base classifiers into an ensemble classifier. Experimental results on face recognition tasks demonstrate that SSC-RSDR not only has superior recognition performance with respect to competitive methods, but also is robust against a wide range of values of input parameters. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Graph construction
Semi-supervised classification
Random subspaces
Dimensionality reduction
Ensembles of classifiers
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7.6
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1.3W
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4.5W
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