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General Tensor Spectral Co-clustering for Higher-Order Data

Tao Wu · Austin Benson · David Gleich

Area 5+6+7+8 #134

Keywords: [ Semi-Supervised Learning ] [ Graph-based Learning ] [ Spectral Methods ] [ Clustering ]


Spectral clustering and co-clustering are well-known techniques in data analysis, and recent work has extended spectral clustering to square, symmetric tensors and hypermatrices derived from a network. We develop a new tensor spectral co-clustering method that simultaneously clusters the rows, columns, and slices of a nonnegative three-mode tensor and generalizes to tensors with any number of modes. The algorithm is based on a new random walk model which we call the super-spacey random surfer. We show that our method out-performs state-of-the-art co-clustering methods on several synthetic datasets with ground truth clusters and then use the algorithm to analyze several real-world datasets.

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