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• W3105254673 abstract "Tensor factorizations with nonnegative constraints have found application in analyzing data from cyber traffic, social networks, and other areas. We consider application data best described as being generated by a Poisson process (e.g., count data), which leads to sparse tensors that can be modeled by sparse factor matrices. In this paper we investigate efficient techniques for computing an appropriate canonical polyadic tensor factorization based on the Kullback-Leibler divergence function. We propose novel subproblem solvers within the standard alternating block variable approach. Our new methods exploit structure and reformulate the optimization problem as small independent subproblems. We employ bound-constrained Newton and quasi-Newton methods. We compare our algorithms against other codes, demonstrating superior speed for high accuracy results and the ability to quickly find sparse solutions." @default.
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• W3105254673 date "2015-04-30" @default.
• W3105254673 modified "2023-09-26" @default.
• W3105254673 title "Newton-based optimization for Kullback–Leibler nonnegative tensor factorizations" @default.
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• W3105254673 doi "https://doi.org/10.1080/10556788.2015.1009977" @default.
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