109 citations · 579 across the 90 of their papers we have counts for
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Accelerating nuclear-norm regularized low-rank matrix optimization through Burer-Monteiro decomposition
Ching-pei Lee, Ling Liang, Tianyun Tang +1
This work proposes a rapid algorithm, BM-Global, for nuclear-norm-regularized convex and low-rank matrix optimization problems. BM-Global efficiently decreases the objective value…
Learning Graph Laplacian with MCP
Yangjing Zhang, Kim-Chuan Toh, Defeng Sun
We consider the problem of learning a graph under the Laplacian constraint with a non-convex penalty: minimax concave penalty (MCP). For solving the MCP penalized graphical model,…
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Defeng Sun, Kim-Chuan Toh, Yancheng Yuan
Clustering is a fundamental problem in unsupervised learning. Popular methods like K-means, may suffer from poor performance as they are prone to get stuck in its local minima. Rec…