IEEE Trans Neural Netw Learn Syst - Kernel reconstruction ICA for sparse representation.

Tópicos

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Resumo

Independent component analysis with soft reconstruction cost (RICA) has been recently proposed to linearly learn sparse representation with an overcomplete basis, and this technique exhibits promising performance even on unwhitened data. However, linear RICA may not be effective for the majority of real-world data because nonlinearly separable data structure pervasively exists in original data space. Meanwhile, RICA is essentially an unsupervised method and does not employ class information. Motivated by the success of the kernel trick that maps a nonlinearly separable data structure into a linearly separable case in a high-dimensional feature space, we propose a kernel RICA (kRICA) model to nonlinearly capture sparse representation in feature space. Furthermore, we extend the unsupervised kRICA to a supervised one by introducing a class-driven discrimination constraint, such that the data samples from the same class are well represented on the basis of the corresponding subset of basis vectors. This discrimination constraint minimizes inhomogeneous representation energy and maximizes homogeneous representation energy simultaneously, which is essentially equivalent to maximizing between-class scatter and minimizing within-class scatter at the same time in an implicit manner. Experimental results demonstrate that the proposed algorithm is more effective than other state-of-the-art methods on several datasets.

Resumo Limpo

independ compon analysi soft reconstruct cost rica recent propos linear learn spars represent overcomplet basi techniqu exhibit promis perform even unwhiten data howev linear rica may effect major realworld data nonlinear separ data structur pervas exist origin data space meanwhil rica essenti unsupervis method employ class inform motiv success kernel trick map nonlinear separ data structur linear separ case highdimension featur space propos kernel rica krica model nonlinear captur spars represent featur space furthermor extend unsupervis krica supervis one introduc classdriven discrimin constraint data sampl class well repres basi correspond subset basi vector discrimin constraint minim inhomogen represent energi maxim homogen represent energi simultan essenti equival maxim betweenclass scatter minim withinclass scatter time implicit manner experiment result demonstr propos algorithm effect stateoftheart method sever dataset

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