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ECCV
2002
Springer
14 years 9 months ago
Representing Edge Models via Local Principal Component Analysis
Edge detection depends not only upon the assumed model of what an edge is, but also on how this model is represented. The problem of how to represent the edge model is typically ne...
Patrick S. Huggins, Steven W. Zucker
ICML
2007
IEEE
14 years 8 months ago
Full regularization path for sparse principal component analysis
Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the numb...
Alexandre d'Aspremont, Francis R. Bach, Laurent El...
WACV
2008
IEEE
14 years 1 months ago
Object Categorization Based on Kernel Principal Component Analysis of Visual Words
In recent years, many researchers are studying object categorization problem. It is reported that bag of keypoints approach which is based on local features without topological in...
Kazuhiro Hotta
PR
2006
116views more  PR 2006»
13 years 7 months ago
Correspondence matching using kernel principal components analysis and label consistency constraints
This paper investigates spectral approaches to the problem of point pattern matching. We make two contributions. First, we consider rigid point-set alignment. Here we show how ker...
Hongfang Wang, Edwin R. Hancock
TNN
2008
141views more  TNN 2008»
13 years 7 months ago
MPCA: Multilinear Principal Component Analysis of Tensor Objects
This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern rec...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...