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2015

Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications

4 years 8 months ago
Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications
Spectral clustering, a graph partitioning technique, has gained immense popularity in machine learning in the context of unsupervised learning. This is due to convincing empirical studies, elegant approaches involved and the theoretical guarantees provided in the literature. To tackle some challenging problems that arose in computer vision etc., recently, a need to develop spectral methods that incorporate multi-way similarity measures surfaced. This, in turn, leads to a hypergraph partitioning problem. In this paper, we formulate a criterion for partitioning uniform hypergraphs, and show that a relaxation of this problem is related to the multilinear singular value decomposition (SVD) of symmetric tensors. Using this, we provide a spectral technique for clustering based on higher order affinities, and derive a theoretical bound on the error incurred by this method. We also study the complexity of the algorithm and use Nystr¨om’s method and column sampling techniques to develop ap...
Debarghya Ghoshdastidar, Ambedkar Dukkipati
Added 27 Mar 2016
Updated 27 Mar 2016
Type Journal
Year 2015
Where AAAI
Authors Debarghya Ghoshdastidar, Ambedkar Dukkipati
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