2015
DOI: 10.1109/tsp.2014.2385045
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Dictionary Learning Over Distributed Models

Abstract: In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over different spatial locations and it is not feasible to aggregate all dictionaries in one location due to communication and privacy considerations. We first show that the dual function of the inference problem is an aggregation of individual cost functions asso… Show more

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Cited by 95 publications
(68 citation statements)
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“…Then, having conditions (24) and (25) hold on S is equivalent to the following conditions, respectively,…”
Section: Remarkmentioning
confidence: 99%
See 1 more Smart Citation
“…Then, having conditions (24) and (25) hold on S is equivalent to the following conditions, respectively,…”
Section: Remarkmentioning
confidence: 99%
“…where in step (a), we used the Cauchy-Schwartz inequality x T y ≤ |x T y| ≤ x · y , and in step (b) we used (24). (Property 4: Stable Jordan operator) First, we notice that matrix D L,n can be written as…”
Section: Appendix a Properties Of The Energy Operatorsmentioning
confidence: 99%
“…Consensus-based decentralized algorithms to solving optimization problems have been proposed in a number of previous works, e.g., [3,4,[7][8][9][10][11][12]. The convergence of these algorithms have been established in [3,4,7,11,12].…”
Section: Relation To Prior Workmentioning
confidence: 99%
“…Further, diffusion algorithms have been successfully applied to problems in cognitive radio [12], wireless sensor networks [14], detection [18], Kalman filtering and smoothing [3], dictionary learning [8], cooperative reinforcement learning [20], classification [30] and distributed optimization problems in general [6], [7], [11].…”
Section: Introductionmentioning
confidence: 99%