A Generalized Graph Regularized Non-Negative Tucker Decomposition Framework for Tensor Data Representation
IEEE Transactions on Cybernetics(2022)
摘要
Non-negative Tucker decomposition (NTD) is one of the most popular techniques for tensor data representation. To enhance the representation ability of NTD by multiple intrinsic cues, that is, manifold structure and supervisory information, in this article, we propose a generalized graph regularized NTD (GNTD) framework for tensor data representation. We first develop the unsupervised GNTD (UGNTD) ...
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关键词
Tensile stress,Manifolds,Laplace equations,Matrix decomposition,Learning systems,Convergence,Automation
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