A Low Tensor-Rank Representation Approach for Clustering of Imaging Data

This letter proposes an algorithm for clustering of two-dimensional data. Instead of “flattening” data into vectors, the proposed algorithm keeps samples as matrices and stores them as lateral slices in a third-order tensor. It is then assumed that the samples lie near a union of free submodules and their representations under this model are obtained by imposing a low tensor-rank constraint and a structural constraint on the representation tensor. Clustering is carried out using an affinity matrix calculated from the representation tensor. Effectiveness of the proposed algorithm and its superiority over existing methods are demonstrated through experiments on two image datasets.

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