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Semi-supervised sub-manifold discriminant analysis
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DOI：10.1016/j.patrec.2008.05.024.png)
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摘要
En 中文
In this paper, we present a semi-supervised sub-manifold discriminant analysis algorithm. To separate each sub-manifold constructed by each class, we define the within-manifold scatter, between-manifold scatter and total-manifold scatter matrices. The scatter matrices are robust to outlier and diverse-density clusters. Kernelization and direct non-linear embedding are also developed. Experimental results show that our approach can give competitive results in comparison to the state-of-the-art algorithms. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
semi-supervised learning
dimensionality reduction
sub-manifold discriminative embedding
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
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