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Proceedings Paper

Adaptive graph construction for Isomap manifold learning
Author(s): Loc Tran; Zezhong Zheng; Guoqing Zhou; Jiang Li
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Paper Abstract

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ1 norm. The ℓ1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap.

Paper Details

Date Published: 16 March 2015
PDF: 7 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939904 (16 March 2015); doi: 10.1117/12.2082646
Show Author Affiliations
Loc Tran, Old Dominion Univ. (United States)
Zezhong Zheng, Univ. of Electronic Science and Technology of China (China)
Guoqing Zhou, Guilin Univ. of Technology (China)
Jiang Li, Old Dominion Univ. (United States)
Guilin Univ. of Technology (China)


Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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