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A novel nonlinear feature extraction method with fuzzy set theory, called KFMSD (kernel based fuzzy maximum scatter difference), is proposed. The proposed method first maps the input data into a potentially much higher dimensional feature space by virtue of nonlinear kernel trick, and thus, the problem of nonlinear feature extraction is overcome. Then, the fuzzy maximum scatter difference is performed...
Locally discriminating projection (LDP) is a new subspace feature extraction method which takes special consideration of both the local information and the class information. As the LDP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locally preserving projection (KLDP). The proposed method consists...
Non-locality preserving projection (NLPP) is a kind of feature extraction technique based on the characterization of the non-local scatter. Due to NLPP is a linear algorithm in nature, it cannot address nonlinear problem in recognition, so a novel subspace method, called Kernel Non-locality Preserving Projection (KNLPP) discriminant analysis, is proposed for face recognition. Experimental results...
Scaling the number of cores on processor chips has become the trend for current semiconduction industry (i.e. Intel/AMD many-core CPU, Nvida GPU etc). Current software development should take advantage of those multi-core platforms to achieve high performance. But it is a challenging task to develop parallel software on multiple processor because of the well known problems such as deadlock, load balancing,...
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