FADA: An Efficient Dimension Reduction Scheme for Image Classification

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dc.contributor.authorLu, Yijuan
dc.contributor.authorMa, Jingsheng
dc.contributor.authorTian, Qi
dc.date.accessioned2023-10-24T14:17:53Z
dc.date.available2023-10-24T14:17:53Z
dc.date.issued2007-12
dc.description.abstractThis paper develops a novel and efficient dimension reduction scheme--Fast Adaptive Discriminant Analysis (FADA). FADA can find a good projection with adaptation to different sample distributions and discover the classification in the subspace with naïve Bayes classifier. FADA overcomes the high computational cost problem of current Adaptive Discriminant Analysis (ADA) and also alleviates the overfitting problem implicitly caused by ADA. FADA is tested and evaluated using synthetic dataset, COREL dataset and three different face datasets. The experimental results show FADA is more effective and computationally more efficient than ADA for image classification.
dc.description.departmentComputer Science
dc.description.sponsorshipThis work was supported in part by Army Research Office (ARO) grant under W911NF-05-1-0404, by Department of Homeland Security (DHS) and by the San Antonio Life Science Institute (SALSI).
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2130
dc.language.isoen_US
dc.publisherUTSA Department of Computer Science
dc.relation.ispartofseriesTechnical Report; CS-TR-2007-010
dc.subjectadaptive discriminant analysis
dc.subjectimage classification
dc.titleFADA: An Efficient Dimension Reduction Scheme for Image Classification
dc.typeTechnical Report

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