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A Generalized Graph Regularized Non-Negative Tucker Decomposition Framework for Tensor Data Representation
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DOI：10.1109/TCYB.2020.2979344.png)
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Abstract
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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) method by constructing the nearest neighbor graph to maintain the intrinsic manifold structure of tensor data. Then, when limited must-link and cannot-link constraints are given, unlike most existing semisupervised learning methods that only use the pregiven supervisory information, we propagate the constraints through the entire dataset and then build a semisupervised graph weight matrix by which we can formulate the semisupervised GNTD (SGNTD). Moreover, we develop a fast and efficient alternating proximal gradient-based algorithm to solve the optimization problem and show its convergence and correctness. The experimental results on unsupervised and semisupervised clustering tasks using four image datasets demonstrate the effectiveness and high efficiency of the proposed methods.
Keywords:
Tensile stress
Manifolds
Laplace equations
Matrix decomposition
Learning systems
Convergence
Automation
Clustering
graph Laplacian
non-negative Tucker decomposition (NTD)
tensor decomposition
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10.5
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1.1W
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5.0W
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