Distinguishing variance embedding

Authors:
Wang Qinggang;Li Jianwei;Wang Xuchu
Affiliations:
Chongqing University, Chongqing 400044, China;Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400044, China;Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400044, China
Venue:
Image and Vision Computing
Year:
2010

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Abstract

Nonlinear dimensionality reduction is a challenging problem encountered in a variety of high dimensional data analysis. Based on the different geometric intuitions of manifolds, maximum variance unfolding (MVU) and Laplacian eigenmaps are designed for detecting the different aspects of data set. In this paper, combining the ideas of MVU and Laplacian eigenmaps, we propose a new nonlinear dimensionality reduction method called distinguishing variance embedding (DVE), which unfolds the data manifold by maximizing the global variance subject to the proximity relation preservation constraint originated in Laplacian eigenmaps. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and rotating objects.