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Proceedings Paper

Graph optimized Laplacian eigenmaps for face recognition
Author(s): F. Dornaika; A. Assoum; Y. Ruichek
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Paper Abstract

In recent years, a variety of nonlinear dimensionality reduction techniques (NLDR) have been proposed in the literature. They aim to address the limitations of traditional techniques such as PCA and classical scaling. Most of these techniques assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. They provide a mapping from the high-dimensional space to the low-dimensional embedding and may be viewed, in the context of machine learning, as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Laplacian Eigenmaps (LE) is a nonlinear graph-based dimensionality reduction method. It has been successfully applied in many practical problems such as face recognition. However the construction of LE graph suffers, similarly to other graph-based DR techniques from the following issues: (1) the neighborhood graph is artificially defined in advance, and thus does not necessary benefit the desired DR task; (2) the graph is built using the nearest neighbor criterion which tends to work poorly due to the high-dimensionality of original space; and (3) its computation depends on two parameters whose values are generally uneasy to assign, the neighborhood size and the heat kernel parameter. To address the above-mentioned problems, for the particular case of the LPP method (a linear version of LE), L. Zhang et al.1 have developed a novel DR algorithm whose idea is to integrate graph construction with specific DR process into a unified framework. This algorithm results in an optimized graph rather than a predefined one.

Paper Details

Date Published: 8 February 2015
PDF: 10 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 94060E (8 February 2015); doi: 10.1117/12.2078277
Show Author Affiliations
F. Dornaika, Univ. of the Basque Country (Spain)
Basque Foundation for Science (Spain)
A. Assoum, Lebanese Univ. (Lebanon)
Y. Ruichek, Univ. de Technologie de Belfort-Montbéliard (France)

Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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