222 citations · 311 across the 6 of their papers we have counts for
6 papers
Greedy Approaches to Symmetric Orthogonal Tensor Decomposition
Cun Mu, Daniel Hsu, Donald Goldfarb
Finding the symmetric and orthogonal decomposition (SOD) of a tensor is a recurring problem in signal processing, machine learning and statistics. In this paper, we review, establi…
Using Negative Curvature in Solving Nonlinear Programs
Donald Goldfarb, Cun Mu, John Wright +1
Minimization methods that search along a curvilinear path composed of a non-ascent nega- tive curvature direction in addition to the direction of steepest descent, dating back to t…
Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors
Cun Mu, Daniel Hsu, Donald Goldfarb
Many idealized problems in signal processing, machine learning and statistics can be reduced to the problem of finding the symmetric canonical decomposition of an underlying symmet…
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
Cun Mu, Yuqian Zhang, John Wright +1
Recovering matrices from compressive and grossly corrupted observations is a fundamental problem in robust statistics, with rich applications in computer vision and machine learnin…
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Cun Mu, Bo Huang, John Wright +1
Recovering a low-rank tensor from incomplete information is a recurring problem in signal processing and machine learning. The most popular convex relaxation of this problem minimi…
Toward Guaranteed Illumination Models for Non-Convex Objects
Yuqian Zhang, Cun Mu, Han-wen Kuo +1
Illumination variation remains a central challenge in object detection and recognition. Existing analyses of illumination variation typically pertain to convex, Lambertian objects,…