Learning probabilistic kernel feature subspace with side-information for classification

Keywords

Kernel principal component analysis, Pattern recognition (psychology), Kernel (algebra), Artificial intelligence, Subspace topology

References

• Selection of Relevant Features and Examples in Machine LearningAvrim L. Bluma, Pat Langley — 1997
• Ensemble of classifiers to improve accuracy of the CLIP4 machine-learning algorithmLukasz Kurgan, Krzysztof J. Cios — 2002
• The FERET evaluation methodology for face-recognition algorithmsP. Jonathon Phillips, Hyeonjoon Moon, Syed A. Rizvi — 1997
• Probabilistic Principal Component AnalysisMichael E. Tipping, Chris Bishop — 1999
• Probabilistic Visual Learning for Object RepresentationB. Moghaddam, Alex Pentland — 1997
• Knowledge discovery approach to automated cardiac SPECT diagnosisLukasz Kurgan, Krzysztof J. Cios, Ryszard Tadeusiewicz — 2001
• Nonlinear Component Analysis as a Kernel Eigenvalue ProblemBernhard Schölkopf, Alexander J. Smola, Klaus‐Robert Müller — 1998
• Enhancing image and video retrieval: learning via equivalence constraintsTomer Hertz, Noam Shental, Aharon Bar-Hillel — 2003
• Learning Distance Functions using Equivalence RelationsAharon Bar-Hillel, Tomer Hertz, Noam Shental — 2003
• The information bottleneck methodPiasini, Eugenio, Fernando C. N. Pereira, William Bialek — 2000
• Neural Networks for Pattern RecognitionChris Bishop — 1995
• Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of PatternsYachen Lin — 2002
• Adjustment Learning and Relevant Component AnalysisN. Shental, T. Hertz, D. Weinshall — 2002

Cited by

• Quasi-objective nonlinear principal component analysisBei-Wei Lu, Lionel Pandolfo — 2011

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