IEEE Trans Pattern Anal Mach Intell - Simultaneous Tensor Decomposition and Completion Using Factor Priors.

Tópicos

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Resumo

Tensor completion, which is a high-order extension of matrix completion, has generated a great deal of research interest in recent years. Given a tensor with incomplete entries, existing methods use either factorization or completion schemes to recover the missing parts. However, as the number of missing entries increases, factorization schemes may overfit the model because of incorrectly predefined ranks, while completion schemes may fail to interpret the model factors. In this paper, we introduce a novel concept: complete the missing entries and simultaneously capture the underlying model structure. To this end, we propose a method called Simultaneous Tensor Decomposition and Completion (STDC) that combines a rank minimization technique with Tucker model decomposition. Moreover, as the model structure is implicitly included in the Tucker model, we use factor priors, which are usually known a priori in real-world tensor objects, to characterize the underlying joint-manifold drawn from the model factors. We conducted experiments to empirically verify the convergence of our algorithm on synthetic data, and evaluate its effectiveness on various kinds of real-world data. The results demonstrate the efficacy of the proposed method and its potential usage in tensor-based applications. It also outperforms state-of-the-art methods on multilinear model analysis and visual data completion tasks.

Resumo Limpo

tensor complet highord extens matrix complet generat great deal research interest recent year given tensor incomplet entri exist method use either factor complet scheme recov miss part howev number miss entri increas factor scheme may overfit model incorrect predefin rank complet scheme may fail interpret model factor paper introduc novel concept complet miss entri simultan captur under model structur end propos method call simultan tensor decomposit complet stdc combin rank minim techniqu tucker model decomposit moreov model structur implicit includ tucker model use factor prior usual known priori realworld tensor object character under jointmanifold drawn model factor conduct experi empir verifi converg algorithm synthet data evalu effect various kind realworld data result demonstr efficaci propos method potenti usag tensorbas applic also outperform stateoftheart method multilinear model analysi visual data complet task

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