Hypergraph Clustering Using a New Laplacian Tensor with Applications in Image Processing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)1157-1178
Journal / PublicationSIAM Journal on Imaging Sciences
Volume13
Issue number3
Online published13 Jul 2020
Publication statusPublished - 2020

Abstract

In this paper, we consider the multiclass clustering problem involving a hypergraph model. Funda-mentally, we study a new normalized Laplacian tensor of an even-uniform weighted hypergraph. The hypergraph's connectivity is related with the second smallest Z-eigenvalue of the proposed Laplacian tensor. Particularly, an analogue of fractional Cheeger inequality holds. Next, we generalize the Laplacian tensor based approach from biclustering to multiclass clustering. A tensor optimization model with an orthogonal constraint is established and analyzed. Finally, we apply our hypergraph clustering approach to image segmentation and motion segmentation problems. Experimental results demonstrate that our method is effective.

Research Area(s)

  • Hypergraph clustering, Hypergraph partitioning, Image processing, Laplacian tensor, Optimization, Stiefel manifold, Tensor eigenvalue