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Multi-class object recognition using boosted linear discriminant analysis combined with masking covariance matrix method
Fourth IEEE International Conference on Computer Vision Systems (ICVS'06)
We propose a new algorithm, boosted linear discriminant analysis (bLDA), for classification of a non-linear pattern distribution, and masking covariance matrix method (MCM) for robust and fast computation of object recognition. bLDA integrates classifiers on multiple linear discriminant spaces. Each linear discriminant space is spanned by eigenvectors so as to maximize ratio of within-class variance and between-class variance of training data. The weights of samples are updated for eachdoi:10.1109/icvs.2006.44 dblp:conf/icvs/Tanigawa06 fatcat:7secof45wjfktnmdqhkfn3epqa