Clustering-Aware Structure-Constrained Low-Rank Submodule Clustering

In this paper, a new clustering algorithm for two-dimensional data is proposed. Unlike most conventional clustering methods which are derived for dealing with matrices, the proposed algorithm performs clustering in a third-order tensor space. The images are then assumed to be drawn from a mixture of low-dimensional free submodules. The proposed method integrates spectral clustering into the optimization problem, thereby overcomes the shortcomings of existing techniques by its ability to perform optimal clustering of the submodules. An efficient algorithm via a combination of an alternating direction method of multipliers and spectral clustering is developed to find the representation tensor and the segmentation simultaneously. The effectiveness of the proposed method is demonstrated through experiments on three image datasets.