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Semi-supervised ensemble classification in subspaces
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DOI：10.1016/j.asoc.2011.12.019.png)
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摘要
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Graph-based semi-supervised classification depends on a well-structured graph. However, it is difficult to construct a graph that faithfully reflects the underlying structure of data distribution, especially for data with a high dimensional representation. In this paper, we focus on graph construction and propose a novel method called semi-supervised ensemble classification in subspaces, SSEC in short. Unlike traditional methods that execute graph-based semi-supervised classification in the original space, SSEC performs semi-supervised linear classification in subspaces. More specifically, SSEC first divides the original feature space into several disjoint feature subspaces. Then, it constructs a neighborhood graph in each subspace, and trains a semi-supervised linear classifier on this graph, which will serve as the base classifier in an ensemble. Finally, SSEC combines the obtained base classifiers into an ensemble classifier using the majority-voting rule. Experimental results on facial images classification show that SSEC not only has higher classification accuracy than the competitive methods, but also can be effective in a wide range of values of input parameters. (C) 2012 Elsevier B. V. All rights reserved.
Keyword:
Graph construction
Semi-supervised classification
High dimensional data
Subspaces
Ensemble classification
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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引用论文
Adaptive Impedance Control to Enhance Human Skill on a Haptic Interface System自适应阻抗控制以增强触觉接口系统上的人类技能
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