IEEE Trans Image Process - Enhancing Low-Rank Subspace Clustering by Manifold Regularization.

Tópicos

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Resumo

Recently, low-rank representation (LRR) method has achieved great success in subspace clustering (SC), which aims to cluster the data points that lie in a union of low-dimensional subspace. Given a set of data points, LRR seeks the lowest rank representation among the many possible linear combinations of the bases in a given dictionary or in terms of the data itself. However, LRR only considers the global Euclidean structure, while the local manifold structure, which is often important for many real applications, is ignored. In this paper, to exploit the local manifold structure of the data, a manifold regularization characterized by a Laplacian graph has been incorporated into LRR, leading to our proposed Laplacian regularized LRR (LapLRR). An efficient optimization procedure, which is based on alternating direction method of multipliers (ADMM), is developed for LapLRR. Experimental results on synthetic and real data sets are presented to demonstrate that the performance of LRR has been enhanced by using the manifold regularization.

Resumo Limpo

recent lowrank represent lrr method achiev great success subspac cluster sc aim cluster data point lie union lowdimension subspac given set data point lrr seek lowest rank represent among mani possibl linear combin base given dictionari term data howev lrr consid global euclidean structur local manifold structur often import mani real applic ignor paper exploit local manifold structur data manifold regular character laplacian graph incorpor lrr lead propos laplacian regular lrr laplrr effici optim procedur base altern direct method multipli admm develop laplrr experiment result synthet real data set present demonstr perform lrr enhanc use manifold regular

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