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ICPR
2008
IEEE
14 years 4 months ago
Boosting Gaussian mixture models via discriminant analysis
The Gaussian mixture model (GMM) can approximate arbitrary probability distributions, which makes it a powerful tool for feature representation and classification. However, it su...
Hao Tang, Thomas S. Huang
ICMCS
2005
IEEE
229views Multimedia» more  ICMCS 2005»
14 years 3 months ago
A methodology for improving recognition rate of linear discriminant analysis in video-based face recognition using support vecto
This paper proposes a two-step methodology for improving the discriminatory power of Linear Discriminant Analysis (LDA) for video-based human face recognition. Results indicate th...
Sreekar Krishna, Sethuraman Panchanathan
CVPR
2010
IEEE
14 years 2 months ago
Bayes Optimal Kernel Discriminant Analysis
Kernel methods provide an efficient mechanism to derive nonlinear algorithms. In classification problems as well as in feature extraction, kernel-based approaches map the original...
Di You, Aleix Martinez
CSB
2005
IEEE
165views Bioinformatics» more  CSB 2005»
13 years 12 months ago
Sequential Diagonal Linear Discriminant Analysis (SeqDLDA) for Microarray Classification and Gene Identification
In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA),...
Roger Pique-Regi, Antonio Ortega, Shahab Asgharzad...
TNN
2008
105views more  TNN 2008»
13 years 9 months ago
Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
Abstract--High-dimensional data are common in many domains, and dimensionality reduction is the key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is ...
Shuiwang Ji, Jieping Ye