Distributed efficient multimodal data clustering

Jia Chen, Ioannis D. Schizas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Clustering of multimodal data according to their information content is considered in this paper. Statistical correlations present in data that contain similar information are exploited to perform the clustering task. Specifically, multiset canonical correlation analysis is equipped with norm-one regularization mechanisms to identify clusters within different types of data that share the same information content. A pertinent minimization formulation is put forth, while block coordinate descent is employed to derive a batch clustering algorithm which achieves better clustering performance than existing alternatives. Distributed implementations are also considered to cluster spatially clustered data utilizing the alternating direction method of multipliers. Relying on subgradient descent, an online clustering approach is derived which substantially lowers computational complexity compared to the batch approaches. Numerical tests demonstrate that the proposed schemes outperform existing alternatives.

Original languageEnglish (US)
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9780992862671
StatePublished - Oct 23 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: Aug 28 2017Sep 2 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017


Other25th European Signal Processing Conference, EUSIPCO 2017

Bibliographical note

Funding Information:
Work in this paper is supported by the NSF Grant ECCS 1509780.


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