IEEE Trans Pattern Anal Mach Intell - Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Tópicos

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Resumo

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Sch?lkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Resumo Limpo

perform cluster algorithm high reli represent data input space hilbert space kernel method paper obtain appropri data represent featur select kernel learn within framework local learningbas cluster llc wu schlkopf method can outperform global learningbas one deal highdimension data lie manifold specif associ weight featur kernel incorpor builtin regular llc algorithm take account relev featur kernel cluster accord weight estim iter cluster process show result weight regular addit constraint weight equival known sparsepromot penalti henc weight irrelev featur kernel can shrunk toward zero extens experi show efficaci propos method benchmark data set

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