AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A graph regularized non-negative bi-level form of triple decomposition (GNBTD) model based on TriD that can extract the low-dimensional part-based representations while maintaining geometrical information from high-dimensional tensor data is proposed.
A novel multi-view clustering model based on non-negative tensor factorization (NTF), which employs multi-level fusion (both data-level and decision-level) to achieve view-consistent labels for multi-view data is proposed.
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 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.
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.
A novel semi-tensor product for third-order tensors under the t-product framework induced by arbitrary invertible linear transforms is introduced, yielding an accelerated multi-term randomized semi-tensor product SVD algorithm that achieves a balance between reconstruction accuracy and computational efficiency.
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.
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