Automatic dimensionality estimation for manifold learning through optimal feature selection

Authors:
Fadi Dornaika;Ammar Assoum;Bogdan Raducanu
Affiliations:
University of the Basque Country UPV/EHU, San Sebastian, Spain,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain;LaMA Laboratory, Lebanese University, Tripoli, Lebanon;Computer Vision Center, Bellaterra, Spain
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis.