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15 November 2007 Boosting bootstrap FLD subspaces for multiclass problem
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Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 678827 (2007)
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
In this paper an ensemble feature extraction algorithm is proposed based on Adaboost.M2 for multiclass classification problem. The proposed algorithm makes no assumption about the distribution of the data and primarily performs by directly selecting the discriminant features with the minimum training error, which can overcome the main drawbacks of the traditional methods, such as Principle Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLD) and Nonparametric Discriminant Analysis (NDA). The proposed method first samples a large number of bootstrap training subsets from the original training set and implements FLD in each subset to get a large number of bootstrap FLD projections. Then at each step of Adaboost.M2, the projection with the minimum weighted K Nearest Neighbor (KNN) classification error is selected from a pool of linear projections to combine the final strong classifier. Experimental results on three real-world data sets demonstrate that the proposed algorithm is superior to other feature extraction techniques.
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Tuo Wang, Daoyi Shen, Lei Wang, and Nenghai Yu "Boosting bootstrap FLD subspaces for multiclass problem", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678827 (15 November 2007);

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