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MVA
2007
154views Computer Vision» more  MVA 2007»
13 years 10 months ago
Fisher Non-negative Matrix Factorization with Pairwise Weighting
Non-negative matrix factorization (NMF) is a powerful feature extraction method for finding parts-based, linear representations of non-negative data . Inherently, it is unsupervis...
Xi Li, Kazuhiro Fukui
SIBGRAPI
2005
IEEE
14 years 2 months ago
A Maximum Uncertainty LDA-Based Approach for Limited Sample Size Problems : With Application to Face Recognition
A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recog...
Carlos E. Thomaz, Duncan Fyfe Gillies
FGR
2004
IEEE
159views Biometrics» more  FGR 2004»
14 years 15 days ago
Null Space-based Kernel Fisher Discriminant Analysis for Face Recognition
The null space-based LDA takes full advantage of the null space while the other methods remove the null space. It proves to be optimal in performance. From the theoretical analysi...
Wei Liu, Yunhong Wang, Stan Z. Li, Tieniu Tan
FGR
2002
IEEE
150views Biometrics» more  FGR 2002»
14 years 1 months ago
Face Recognition Using Kernel Based Fisher Discriminant Analysis
Qingshan Liu, Rui Huang, Hanqing Lu, Songde Ma
NIPS
2004
13 years 10 months ago
Two-Dimensional Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional d...
Jieping Ye, Ravi Janardan, Qi Li