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PAMI
2008
162views more  PAMI 2008»
13 years 5 months ago
Dimensionality Reduction of Clustered Data Sets
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Guido Sanguinetti
NN
2002
Springer
224views Neural Networks» more  NN 2002»
13 years 5 months ago
Optimal design of regularization term and regularization parameter by subspace information criterion
The problem of designing the regularization term and regularization parameter for linear regression models is discussed. Previously, we derived an approximation to the generalizat...
Masashi Sugiyama, Hidemitsu Ogawa
SAC
2006
ACM
14 years 5 days ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
ACMACE
2007
ACM
13 years 10 months ago
Application of dimensionality reduction techniques to HRTFS for interactive virtual environments
Fundamental to the generation of 3D audio is the HRTF processing of acoustical signals. Unfortunately, given the high dimensionality of HRTFs, incorporating them into dynamic/inte...
Bill Kapralos, Nathan Mekuz
CIVR
2008
Springer
271views Image Analysis» more  CIVR 2008»
13 years 8 months ago
Multiple feature fusion by subspace learning
Since the emergence of extensive multimedia data, feature fusion has been more and more important for image and video retrieval, indexing and annotation. Existing feature fusion t...
Yun Fu, Liangliang Cao, Guodong Guo, Thomas S. Hua...