Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29739
Title: Semi-supervised classification based on random subspace dimensionality reduction
Authors: Yu, G
Zhang, G
Domeniconi, C
Yu, Z
You, J 
Keywords: Dimensionality reduction
Ensembles of classifiers
Graph construction
Random subspaces
Semi-supervised classification
Issue Date: 2012
Publisher: Elsevier Sci Ltd
Source: Pattern Recognition, 2012, v. 45, no. 3, p. 1119-1135 How to cite?
Journal: Pattern Recognition 
Abstract: 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.
URI: http://hdl.handle.net/10397/29739
DOI: 10.1016/j.patcog.2011.08.024
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