TY  - CONF
AU  - Fadi Dornaika
AU  - A.Assoum
AU  - Bogdan Raducanu
A2  - SSPR&SPR
PY  - 2012//
TI  - Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection
T2  - LNCS
BT  - Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop
SP  - 575
EP  - 583
VL  - 7626
PB  - Springer Berlin Heidelberg
N2  - 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.
SN  - 0302-9743
SN  - 978-3-642-34165-6
L1  - http://refbase.cvc.uab.es/files/DAR2012.pdf
UR  - http://dx.doi.org/10.1007/978-3-642-34166-3_63
N1  - OR;MV
ID  - Fadi Dornaika2012
ER  -