Extended local tangent space alignment for classification

The local tangent space alignment (LTSA) has demonstrated promising results in finding meaningful low-dimensional structures hidden in high-dimensional data. However, LTSA may have a limited effectiveness on the data which are organized in multiple classes or contain noisy points. In this paper, the...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 77; no. 1; pp. 261 - 266
Main Authors Wang, Jing, Jiang, Wenxian, Gou, Jin
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.02.2012
Subjects
Alignment
Classification
Manifold learning
Reconstruction
Reconstruction weights
Rescale
Robustness
Tangents
Manifold learning
Rescale
Reconstruction weights
Classification
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Summary:The local tangent space alignment (LTSA) has demonstrated promising results in finding meaningful low-dimensional structures hidden in high-dimensional data. However, LTSA may have a limited effectiveness on the data which are organized in multiple classes or contain noisy points. In this paper, the distances between the samples and their neighbors are rescaled by using the reconstruction weights to overcome the limitation. An extension of LTSA is proposed based on the local rescaled distance matrix. Numerical experiments on both synthetic and real-world data sets are used to show the improvement of our extension for classification and the robustness to noisy data.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2011.08.025