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 language||English (US)|
|Title of host publication||25th European Signal Processing Conference, EUSIPCO 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Oct 23 2017|
|Event||25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece|
Duration: Aug 28 2017 → Sep 2 2017
|Name||25th European Signal Processing Conference, EUSIPCO 2017|
|Other||25th European Signal Processing Conference, EUSIPCO 2017|
|Period||8/28/17 → 9/2/17|
Bibliographical noteFunding Information:
Work in this paper is supported by the NSF Grant ECCS 1509780.