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Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques

Ghosdastidar, Debarghya and Dukkipati, Ambedkar (2017) Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. In: JOURNAL OF MACHINE LEARNING RESEARCH, 18 .

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Abstract

In a series of recent works, we have generalised the consistency results in the stochastic block model literature to the case of uniform and non-uniform hypergraphs. The present paper continues the same line of study, where we focus on partitioning weighted uniform hypergraphs-a problem often encountered in computer vision. This work is motivated by two issues that arise when a hypergraph partitioning approach is used to tackle computer vision problems: (i) The uniform hypergraphs constructed for higher-order learning contain all edges, but most have negligible weights. Thus, the adjacency tensor is nearly sparse, and yet, not binary. (ii) A more serious concern is that standard partitioning algorithms need to compute all edge weights, which is computationally expensive for hypergraphs. This is usually resolved in practice by merging the clustering algorithm with a tensor sampling strategy-an approach that is yet to be analysed rigorously. We build on our earlier work on partitioning dense unweighted uniform hypergraphs (Ghoshdastidar and Dukkipati, ICML, 2015), and address the aforementioned issues by proposing provable and efficient partitioning algorithms. Our analysis justifies the empirical success of practical sampling techniques. We also complement our theoretical findings by elaborate empirical comparison of various hypergraph partitioning schemes.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the MICROTOME PUBL, 31 GIBBS ST, BROOKLINE, MA 02446 USA
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 12 Aug 2017 06:57
Last Modified: 12 Aug 2017 06:57
URI: http://eprints.iisc.ac.in/id/eprint/57632

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