@inproceedings{ab7442951a9d4b03bac852d4ce79cf1a,
title = "Clustering Based on MultiView Diffusion Maps",
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.",
keywords = "Clustering, Diffusion Maps, Dimensionality Reduction",
author = "Ofir Lindenbaum and Arie Yeredor and Amir Averbuch",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 ; Conference date: 12-12-2016 Through 15-12-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/icdmw.2016.0109",
language = "الإنجليزيّة",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "740--747",
editor = "Carlotta Domeniconi and Francesco Gullo and Francesco Bonchi and Josep Domingo-Ferrer and Ricardo Baeza-Yates and Zhi-Hua Zhou and Xindong Wu",
booktitle = "Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016",
address = "الولايات المتّحدة",
}