On using prototype reduction schemes and classifier fusion strategies to optimize kernel-based nonlinear subspace methods Export

@article{citeulike:1533739, abstract = {In kernel-based nonlinear subspace (KNS) methods, the length of the projections onto the principal component directions in the feature space, is computed using a kernel matrix, K, whose dimension is equivalent to the number of sample data points. Clearly this is problematic, especially, for large data sets. In this paper, we solve this problem by subdividing the data into smaller subsets, and utilizing a prototype reduction scheme (PRS) as a preprocessing module, to yield more refined representative prototypes. Thereafter, a classifier fusion strategy (CFS) is invoked as a postprocessing module, to combine the individual KNS classification results to derive a consensus decision. Essentially, the PRS is used to yield computational advantage, and the CFS, in turn, is used to compensate for the decreased efficiency caused by the data set division. Our experimental results demonstrate that the proposed mechanism significantly reduces the prototype extraction time as well as the computation time without sacrificing the classification accuracy. The results especially demonstrate a significant computational advantage for large data sets within a parallel processing philosophy.}, author = {Kim, Sang-Woon and Oommen, B. J.}, citeulike-article-id = {1533739}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1388270}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, keywords = {dissimilary\_representation, sample, selection}, number = {3}, pages = {455--460}, posted-at = {2007-08-03 18:08:52}, priority = {0}, title = {On using prototype reduction schemes and classifier fusion strategies to optimize kernel-based nonlinear subspace methods}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1388270}, volume = {27}, year = {2005} }

TY - JOUR ID - citeulike:1533739 L3 - citeulike-article-id:1533739 N2 - In kernel-based nonlinear subspace (KNS) methods, the length of the projections onto the principal component directions in the feature space, is computed using a kernel matrix, K, whose dimension is equivalent to the number of sample data points. Clearly this is problematic, especially, for large data sets. In this paper, we solve this problem by subdividing the data into smaller subsets, and utilizing a prototype reduction scheme (PRS) as a preprocessing module, to yield more refined representative prototypes. Thereafter, a classifier fusion strategy (CFS) is invoked as a postprocessing module, to combine the individual KNS classification results to derive a consensus decision. Essentially, the PRS is used to yield computational advantage, and the CFS, in turn, is used to compensate for the decreased efficiency caused by the data set division. Our experimental results demonstrate that the proposed mechanism significantly reduces the prototype extraction time as well as the computation time without sacrificing the classification accuracy. The results especially demonstrate a significant computational advantage for large data sets within a parallel processing philosophy. IS - 3 JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on EP - 460 TI - On using prototype reduction schemes and classifier fusion strategies to optimize kernel-based nonlinear subspace methods VL - 27 SP - 455 KW - dissimilary_representation KW - sample KW - selection AU - Kim, Sang-Woon AU - Oommen, BJ PY - 2005/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1388270 ER -