Friday, January 09, 2015

Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data

Waheed just mentioned the following to me: Distributed computating of Dictionary Learning.

This paper studies the problem of data-adaptive representations for big, distributed data. It is assumed that a number of geographically-distributed, interconnected sites have massive local data and they are interested in collaboratively learning a low-dimensional geometric structure underlying these data. In contrast to some of the previous works on subspace-based data representations, this paper focuses on the geometric structure of a union of subspaces (UoS). In this regard, it proposes a distributed algorithm--termed cloud K-SVD--for collaborative learning of a UoS structure underlying distributed data of interest. The goal of cloud K-SVD is to learn an overcomplete dictionary at each individual site such that every sample in the distributed data can be represented through a small number of atoms of any one of the learned dictionaries. Cloud K-SVD accomplishes this goal without requiring communication of individual data samples between different sites. This paper also provides a rigorous analysis of cloud K-SVD that gives insights into its properties as well as deviations of the dictionaries learned at individual sites from a centralized solution in terms of different measures of local/global data and topology of the interconnections. Finally, the paper also numerically illustrates the efficacy of cloud K-SVD on both real and synthetic distributed data.
Thanks Waheed !
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