no code implementations • 15 Feb 2023 • Yuning Yang
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.
no code implementations • 5 Dec 2020 • Xianpeng Mao, Yuning Yang
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.
1 code implementation • 3 May 2020 • Yuning Yang, Yunlong Feng
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
1 code implementation • 25 Nov 2019 • Yuning Yang
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
1 code implementation • 29 Sep 2019 • Yuning Yang, Guoyin Li
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
no code implementations • 7 Mar 2015 • Yuning Yang, Siamak Mehrkanoon, Johan A. K. Suykens
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.