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PubMed · 15971917

Kernel pooled local subspaces for classification.

Peng Zhang·Jing Peng·Carlotta Domeniconi
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Abstract

We investigate the use of subspace analysis methods for learning low-dimensional representations for classification. We propose a kernel-pooled local discriminant subspace method and compare it against competing techniques: kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA) in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results using several data sets demonstrate the effectiveness and performance superiority of the kernel-pooled subspace method over competing methods such as KPCA and GDA in some classification problems.

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AlgorithmsArtificial IntelligenceCluster AnalysisComputer GraphicsComputer SimulationFaceFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedInformation Storage and RetrievalModels, BiologicalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedPhotographyReproducibility of ResultsSample SizeSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction TechniqueComparative StudyEvaluation StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

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Peng Zhang, Jing Peng, Carlotta Domeniconi. 2005. Kernel pooled local subspaces for classification.. https://doi.org/10.1109/tsmcb.2005.846641

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