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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2017

02-06-2015 | Original Article

Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing

Authors: Meng Meng, Jia Wei, Jiabing Wang, Qianli Ma, Xuan Wang

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2017

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Abstract

With the rapid growth of high dimensional data, dimensionality reduction is playing a more and more important role in practical data processing and analysing tasks. This paper studies semi-supervised dimensionality reduction using pairwise constraints. In this setting, domain knowledge is given in the form of pairwise constraints, which specifies whether a pair of instances belong to the same class (must-link constraint) or different classes (cannot-link constraint). In this paper, a novel semi-supervised dimensionality reduction method called adaptive semi-supervised dimensionality reduction (ASSDR) is proposed, which can get the optimized low dimensional representation of the original data by adaptively adjusting the weights of the pairwise constraints and simultaneously optimizing the graph construction. Experiments on UCI classification and image recognition show that ASSDR is superior to many existing dimensionality reduction methods.
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Footnotes
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Metadata
Title
Adaptive semi-supervised dimensionality reduction based on pairwise constraints weighting and graph optimizing
Authors
Meng Meng
Jia Wei
Jiabing Wang
Qianli Ma
Xuan Wang
Publication date
02-06-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0380-3

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