148 research outputs found
Nonconvex third-order Tensor Recovery Based on Logarithmic Minimax Function
Recent researches have shown that low-rank tensor recovery based non-convex
relaxation has gained extensive attention. In this context, we propose a new
Logarithmic Minimax (LM) function. The comparative analysis between the LM
function and the Logarithmic, Minimax concave penalty (MCP), and Minimax
Logarithmic concave penalty (MLCP) functions reveals that the proposed function
can protect large singular values while imposing stronger penalization on small
singular values. Based on this, we define a weighted tensor LM norm as a
non-convex relaxation for tensor tubal rank. Subsequently, we propose the
TLM-based low-rank tensor completion (LRTC) model and the TLM-based tensor
robust principal component analysis (TRPCA) model respectively. Furthermore, we
provide theoretical convergence guarantees for the proposed methods.
Comprehensive experiments were conducted on various real datasets, and a
comparison analysis was made with the similar EMLCP method. The results
demonstrate that the proposed method outperforms the state-of-the-art methods
A dual framework for low-rank tensor completion
One of the popular approaches for low-rank tensor completion is to use the
latent trace norm regularization. However, most existing works in this
direction learn a sparse combination of tensors. In this work, we fill this gap
by proposing a variant of the latent trace norm that helps in learning a
non-sparse combination of tensors. We develop a dual framework for solving the
low-rank tensor completion problem. We first show a novel characterization of
the dual solution space with an interesting factorization of the optimal
solution. Overall, the optimal solution is shown to lie on a Cartesian product
of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian
optimization framework for proposing computationally efficient trust region
algorithm. The experiments illustrate the efficacy of the proposed algorithm on
several real-world datasets across applications.Comment: Aceepted to appear in Advances of Nueral Information Processing
Systems (NIPS), 2018. A shorter version appeared in the NIPS workshop on
Synergies in Geometric Data Analysis 201
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