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Local margin based semi-supervised discriminant embedding for visual recognition
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DOI：10.1016/j.neucom.2010.11.004.png)
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摘要
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Most manifold learning algorithms adopt the k nearest neighbors function to construct the adjacency graph. However, severe bias may be introduced in this case if the samples are not uniformly distributed in the ambient space. In this paper a semi-supervised dimensionality reduction method is proposed to alleviate this problem. Based on the notion of local margin, we simultaneously maximize the separability between different classes and estimate the intrinsic geometric structure of the data by both the labeled and unlabeled samples. For high-dimensional data, a discriminant subspace is derived via maximizing the cumulative local margins. Experimental results on high-dimensional classification tasks demonstrate the efficacy of our algorithm. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Dimensionality reduction
Margin
Manifold learning
Semi-supervised learning
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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引用论文
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