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» Diagonal principal component analysis for face recognition
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PR
2007
145views more  PR 2007»
13 years 7 months ago
Face recognition using a kernel fractional-step discriminant analysis algorithm
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kern...
Guang Dai, Dit-Yan Yeung, Yuntao Qian
ICPR
2006
IEEE
14 years 1 months ago
Regularized Locality Preserving Learning of Pre-Image Problem in Kernel Principal Component Analysis
In this paper, we address the pre-image problem in kernel principal component analysis (KPCA). The preimage problem finds a pattern as the pre-image of a feature vector defined in...
Weishi Zheng, Jian-Huang Lai
JMM2
2008
92views more  JMM2 2008»
13 years 7 months ago
Dimensionality Reduction using SOM based Technique for Face Recognition
Unsupervised or Self-Organized learning algorithms have become very popular for discovery of significant patterns or features in the input data. The three prominent algorithms name...
Dinesh Kumar, C. S. Rai, Shakti Kumar
ICIP
2005
IEEE
14 years 9 months ago
Largest-eigenvalue-theory for incremental principal component analysis
In this paper, we present a novel algorithm for incremental principal component analysis. Based on the LargestEigenvalue-Theory, i.e. the eigenvector associated with the largest ei...
Shuicheng Yan, Xiaoou Tang
ACCV
2007
Springer
14 years 1 months ago
Kernel Discriminant Analysis Based on Canonical Differences for Face Recognition in Image Sets
A novel kernel discriminant transformation (KDT) algorithm based on the concept of canonical differences is presented for automatic face recognition applications. For each individu...
Wen-Sheng Vincent Chu, Ju-Chin Chen, Jenn-Jier Jam...