A semi-supervised approach for dimensionality reduction with distributional similarity

Zheng, Feng, Song, Zhan, Shao, Ling, Chung, Ronald, Jia, Kui and Wu, Xinyu (2013) A semi-supervised approach for dimensionality reduction with distributional similarity. Neurocomputing, 103. pp. 210-221. ISSN 0925-2312

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Abstract

Semi-supervised learning has recently received considerable attention in machine learning. In this paper, we propose a novel diffusion maps based semi-supervised algorithm for dimensionality reduction, visualization and data representation. Unlike previous work which uses only geometric information for similarity metric construction, a distributional similarity metric is introduced to modify the geometric relationship of samples. This metric is defined using the posterior probability over the labels of each sample, which is learned through the Expectation–Maximization (EM) algorithm. The Euclidean distance between points on the intrinsic manifold learned by our proposed method is equal to the label-dependent “diffusion distance”, which is modified by the distributional similarity related metric, in the original space. Our algorithm preserves the local manifold structure in addition to separating samples in different classes, thus facilitates the classification. Encouraging experimental results on handwritten digits, Yale faces, UCI data sets and the Weizmann data set show that the algorithm can improve the classification accuracy significantly.

Item Type:	Article
Uncontrolled Keywords:	Diffusion maps; Manifold learning; Label information; Expectation–Maximization; Distributional similarity
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Publisher
Depositing User:	Paul Burns
Date Deposited:	10 Jun 2015 14:40
Last Modified:	12 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/22838

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