Semi-supervised sub-manifold discriminant analysis

Yangqiu Song; Feiping Nie; Changshui Zhang

doi:10.1016/j.patrec.2008.05.024

Semi-supervised sub-manifold discriminant analysis

Yangqiu Song
, Feiping Nie
, Changshui Zhang

Tsinghua University

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	1806-1813
Number of pages	8
Journal	Pattern Recognition Letters
Volume	29
Issue number	13
DOIs	https://doi.org/10.1016/j.patrec.2008.05.024
State	Published - 1 Oct 2008
Externally published	Yes

Keywords

Dimensionality reduction
Semi-supervised learning
Sub-manifold discriminative embedding

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10.1016/j.patrec.2008.05.024

Cite this

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abstract = "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.",

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AU - Zhang, Changshui

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N2 - 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.

AB - 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.

KW - Dimensionality reduction

KW - Semi-supervised learning

KW - Sub-manifold discriminative embedding

UR - https://www.scopus.com/pages/publications/48649090541

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DO - 10.1016/j.patrec.2008.05.024

M3 - 文章

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VL - 29

SP - 1806

EP - 1813

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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ER -

Semi-supervised sub-manifold discriminant analysis

Abstract

Keywords

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