87 citations · 91 across the 14 of their papers we have counts for
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Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
Yisi Luo, Xile Zhao, Zhemin Li +2
Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become on…
Stochastic Variance Reduced Gradient for affine rank minimization problem
Ningning Han, Juan Nie, Jian Lu +1
We develop an efficient stochastic variance reduced gradient descent algorithm to solve the affine rank minimization problem consists of finding a matrix of minimum rank from linea…
A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization
Yihang Gao, Michael K. Ng
The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-convex optimization problems, due to its global con…
Approximate Secular Equations for the Cubic Regularization Subproblem
Yihang Gao, Man-Chung Yue, Michael K. Ng
The cubic regularization method (CR) is a popular algorithm for unconstrained non-convex optimization. At each iteration, CR solves a cubically regularized quadratic problem, calle…
Separable Quaternion Matrix Factorization for Polarization Images
Junjun Pan, Michael K. Ng
Polarization is a unique characteristic of transverse wave and is represented by Stokes parameters. Analysis of polarization states can reveal valuable information about the source…
Deep neural networks for solving large linear systems arising from high-dimensional problems
Yiqi Gu, Michael K. Ng
This paper studies deep neural networks for solving extremely large linear systems arising from highdimensional problems. Because of the curse of dimensionality, it is expensive to…