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Found 6 papers, 3 papers with code
On Finite-Step Convergence of the Non-Greedy Algorithm and Proximal Alternating Minimization Method with Extrapolation for $L_1$-Norm PCA
By recognizing it as a conditional subgradient, we prove that the iterative points generated by the algorithm will be constant in finitely many steps under a certain full-rank assumption; such an assumption can be removed when the projection dimension is one.
Several Approximation Algorithms for Sparse Best Rank-1 Approximation to Higher-Order Tensors
Sparse tensor best rank-1 approximation (BR1Approx), which is a sparsity generalization of the dense tensor BR1Approx, and is a higher-order extension of the sparse matrix BR1Approx, is one of the most important problems in sparse tensor decomposition and related problems arising from statistics and machine learning.
Half-Quadratic Alternating Direction Method of Multipliers for Robust Orthogonal Tensor Approximation
In this paper, based on the maximum a posterior estimation, we derive a robust orthogonal tensor CPD model with Cauchy loss, which is resistant to heavy-tailed noise or outliers.
Optimization and Control
The Epsilon-Alternating Least Squares for Orthogonal Low-Rank Tensor Approximation and Its Global Convergence
The epsilon alternating least squares ($\epsilon$-ALS) is developed and analyzed for canonical polyadic decomposition (approximation) of a higher-order tensor where one or more of the factor matrices are assumed to be columnwisely orthonormal.
Optimization and Control Numerical Analysis Numerical Analysis
Efficiently Maximizing a Homogeneous Polynomial over Unit Sphere without Convex Relaxation
This problem is equivalent to finding the leading eigenvalue of the associated symmetric tensor of higher order, which is nonconvex and NP-hard.
Optimization and Control
Higher order Matching Pursuit for Low Rank Tensor Learning
In this paper, we propose higher order matching pursuit for low rank tensor learning problems with a convex or a nonconvex cost function, which is a generalization of the matching pursuit type methods.
