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      Groupwise Constrained Reconstruction for Subspace Clustering

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          Abstract

          Reconstruction based subspace clustering methods compute a self reconstruction matrix over the samples and use it for spectral clustering to obtain the final clustering result. Their success largely relies on the assumption that the underlying subspaces are independent, which, however, does not always hold in the applications with increasing number of subspaces. In this paper, we propose a novel reconstruction based subspace clustering model without making the subspace independence assumption. In our model, certain properties of the reconstruction matrix are explicitly characterized using the latent cluster indicators, and the affinity matrix used for spectral clustering can be directly built from the posterior of the latent cluster indicators instead of the reconstruction matrix. Experimental results on both synthetic and real-world datasets show that the proposed model can outperform the state-of-the-art methods.

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          Normalized cuts and image segmentation

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            From few to many: illumination cone models for face recognition under variable lighting and pose

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              Mixtures of Probabilistic Principal Component Analyzers

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                Author and article information

                Journal
                18 June 2012
                Article
                1206.4644

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                ICML2012
                cs.LG stat.ML
                icml2012

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