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TKDE
2011
479views more  TKDE 2011»
12 years 11 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang
JMLR
2012
11 years 7 months ago
On Nonparametric Guidance for Learning Autoencoder Representations
Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for...
Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle
CVPR
2003
IEEE
14 years 7 months ago
Kernel Principal Angles for Classification Machines with Applications to Image Sequence Interpretation
We consider the problem of learning with instances defined over a space of sets of vectors. We derive a new positive definite kernel f(A B) defined over pairs of matrices A B base...
Lior Wolf, Amnon Shashua
IVC
2006
187views more  IVC 2006»
13 years 4 months ago
Dynamics of facial expression extracted automatically from video
We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions, including AdaBoost, support vector mach...
Gwen Littlewort, Marian Stewart Bartlett, Ian R. F...
PR
2008
113views more  PR 2008»
13 years 4 months ago
Do unbalanced data have a negative effect on LDA?
For two-class discrimination, Ref. [1] claimed that, when covariance matrices of the two classes were unequal, a (class) unbalanced dataset had a negative effect on the performanc...
Jing-Hao Xue, D. Mike Titterington