Clustering Based on MultiView Diffusion Maps

Ofir Lindenbaum, Arie Yeredor, Amir Averbuch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We consider a reduced dimensionality representation based on multiple views of the same underlying process. These multiple views can be obtained, for example, using several different modalities, measured with different instrumentation or generated based on different methods of feature extractions. Our framework is based on a cross-view random walk process which is restrained to hop between the different views in each time step. The random walk model is constructed using the intrinsic relation within each view as well as the mutual relations between views. Within this framework, multiview diffusion distances are defined which lead to reduced representations for each view. The reduced representations are exploited to perform clustering. The applicability of the multiview approach for clustering is demonstrated on both artificial and real data.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781509054725
StatePublished - 2 Jul 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference16th IEEE International Conference on Data Mining Workshops, ICDMW 2016


  • Clustering
  • Diffusion Maps
  • Dimensionality Reduction


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