TL;DR
A structure-regularized CP (SR-CP) framework that uses a unified quadratic penalty to encode diverse structural priors through positive semidefinite matrices and a soft orthogonality-promoting penalty is further introduced to enhance component distinctiveness and numerical stability.
This work develops efficient algorithms based on the difference-of-convex function algorithm (DCA) and the alternating direction method of multipliers (ADMM) to enhance sparsity and identifiability of the learned factors in separable nonnegative matrix factorization.
This paper proposes a novel Tensor Train (TT)-based tensor-on-tensor regression optimization framework for variable selection based on mode-1 hyperslice sparsity, and designs an alternating iterative algorithm equipped with a preconditioned metric to efficiently solve the proposed model.
The proposed SGLog-γ∗-MSC, a nonconvex multi-view subspace clustering framework that combines graph total variation, an ℓ2,log penalty, and a tensor γ∗ spectral penalty, supports combining robust error modeling with local and shared structural regularization while emphasizing the dataset dependence of individual compon...
A finite-step approximation guarantee that approaches the classical $1-1/e$ factor as direction certification becomes exact and finite-step residual error vanishes is established.
Two weighted Schatten-$p tensor factorization models are proposed under the tensor-tensor product (t-product) framework to address limitations of low-rank tensor factorization and demonstrate competitive reconstruction quality and robustness under various degradation conditions.
Simulations and an empirical application show that SMPCA is more accurate and stable than competitors under heavy-tailed distributions and outlier contamination, while retaining competitive performance under light-tailed settings.
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