Author: Otoom Ahmed
Publisher: Springer Publishing Company
ISSN: 1433-7541
Source: Pattern Analysis and Applications (PAA), Vol.14, Iss.2, 2011-05, pp. : 193-205
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
The curse of dimensionality hinders the effectiveness of density estimation in high dimensional spaces. Many techniques have been proposed in the past to discover embedded, locally linear manifolds of lower dimensionality, including the mixture of principal component analyzers, the mixture of probabilistic principal component analyzers and the mixture of factor analyzers. In this paper, we propose a novel mixture model for reducing dimensionality based on a linear transformation which is not restricted to be orthogonal nor aligned along the principal directions. For experimental validation, we have used the proposed model for classification of five “hard” data sets and compared its accuracy with that of other popular classifiers. The performance of the proposed method has outperformed that of the mixture of probabilistic principal component analyzers on four out of the five compared data sets with improvements ranging from 0.5 to 3.2%. Moreover, on all data sets, the accuracy achieved by the proposed method outperformed that of the Gaussian mixture model with improvements ranging from 0.2 to 3.4%.
Related content
By Carmona Pedro Latorre Pla Filiberto
Conference on Colour in Graphics, Imaging, and Vision, Vol. 2008, Iss. 1, 2008-01 ,pp. :
Contractivity of linear fractional transformations
By Heckmann R.
Theoretical Computer Science, Vol. 279, Iss. 1, 2002-05 ,pp. :
On iterating linear transformations over recognizable sets of integers
By Boigelot B.
Theoretical Computer Science, Vol. 309, Iss. 1, 2003-12 ,pp. :
Maximum likelihood linear transformations for HMM-based speech recognition
By Gales M.J.F.
Computer Speech & Language, Vol. 12, Iss. 2, 1998-04 ,pp. :