Clustering Based on MultiView Diffusion Maps

Ofir Lindenbaum; Arie Yeredor; Amir Averbuch

doi:10.1109/icdmw.2016.0109

Clustering Based on MultiView Diffusion Maps

Ofir Lindenbaum, Arie Yeredor, Amir Averbuch

Tel Aviv University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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 language	English
Title of host publication	Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Editors	Carlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
Publisher	IEEE Computer Society
Pages	740-747
Number of pages	8
ISBN (Electronic)	9781509054725
DOIs	https://doi.org/10.1109/icdmw.2016.0109
State	Published - 2 Jul 2016
Externally published	Yes
Event	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain Duration: 12 Dec 2016 → 15 Dec 2016

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	0
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
Country/Territory	Spain
City	Barcelona
Period	12/12/16 → 15/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Clustering
Diffusion Maps
Dimensionality Reduction

Access to Document

10.1109/icdmw.2016.0109

Cite this

Lindenbaum, O., Yeredor, A., & Averbuch, A. (2016). Clustering Based on MultiView Diffusion Maps. In C. Domeniconi, F. Gullo, F. Bonchi, F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, R. Baeza-Yates, R. Baeza-Yates, Z.-H. Zhou, & X. Wu (Eds.), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 740-747). Article 7836740 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/icdmw.2016.0109

Lindenbaum, Ofir ; Yeredor, Arie ; Averbuch, Amir. / Clustering Based on MultiView Diffusion Maps. Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. editor / Carlotta Domeniconi ; Francesco Gullo ; Francesco Bonchi ; Francesco Bonchi ; Josep Domingo-Ferrer ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Zhi-Hua Zhou ; Xindong Wu. IEEE Computer Society, 2016. pp. 740-747 (IEEE International Conference on Data Mining Workshops, ICDMW).

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abstract = "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.",

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Lindenbaum, O, Yeredor, A & Averbuch, A 2016, Clustering Based on MultiView Diffusion Maps. in C Domeniconi, F Gullo, F Bonchi, F Bonchi, J Domingo-Ferrer, R Baeza-Yates, R Baeza-Yates, R Baeza-Yates, Z-H Zhou & X Wu (eds), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016., 7836740, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 0, IEEE Computer Society, pp. 740-747, 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016, Barcelona, Spain, 12/12/16. https://doi.org/10.1109/icdmw.2016.0109

Clustering Based on MultiView Diffusion Maps. / Lindenbaum, Ofir; Yeredor, Arie; Averbuch, Amir.
Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. ed. / Carlotta Domeniconi; Francesco Gullo; Francesco Bonchi; Francesco Bonchi; Josep Domingo-Ferrer; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Zhi-Hua Zhou; Xindong Wu. IEEE Computer Society, 2016. p. 740-747 7836740 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Lindenbaum O, Yeredor A, Averbuch A. Clustering Based on MultiView Diffusion Maps. In Domeniconi C, Gullo F, Bonchi F, Bonchi F, Domingo-Ferrer J, Baeza-Yates R, Baeza-Yates R, Baeza-Yates R, Zhou ZH, Wu X, editors, Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE Computer Society. 2016. p. 740-747. 7836740. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/icdmw.2016.0109

Clustering Based on MultiView Diffusion Maps

Abstract

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Keywords

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