Search Results for author: Defeng Sun

Found 10 papers, 0 papers with code

Learning Graph Laplacian with MCP

no code implementations22 Oct 2020 Yangjing Zhang, Kim-Chuan Toh, Defeng Sun

Motivated by the observation that the ability of the $\ell_1$ norm in promoting sparsity in graphical models with Laplacian constraints is much weakened, this paper proposes to learn graph Laplacian with a non-convex penalty: minimax concave penalty (MCP).

Estimation of sparse Gaussian graphical models with hidden clustering structure

no code implementations17 Apr 2020 Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang

The sparsity and clustering structure of the concentration matrix is enforced to reduce model complexity and describe inherent regularities.

Efficient algorithms for multivariate shape-constrained convex regression problems

no code implementations26 Feb 2020 Meixia Lin, Defeng Sun, Kim-Chuan Toh

We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables and at least $n(n-1)$ linear inequality constraints, where $n$ is the number of data points.

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems

no code implementations27 Mar 2019 Peipei Tang, Chengjing Wang, Defeng Sun, Kim-Chuan Toh

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for these problems.

A dual Newton based preconditioned proximal point algorithm for exclusive lasso models

no code implementations1 Feb 2019 Meixia Lin, Defeng Sun, Kim-Chuan Toh, Yancheng Yuan

In addition, we derive the corresponding HS-Jacobian to the proximal mapping and analyze its structure --- which plays an essential role in the efficient computation of the PPA subproblem via applying a semismooth Newton method on its dual.

Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm

no code implementations4 Oct 2018 Defeng Sun, Kim-Chuan Toh, Yancheng Yuan

The perfect recovery properties of the convex clustering model with uniformly weighted all pairwise-differences regularization have been proved by Zhu et al. (2014) and Panahi et al. (2017).

A Fast Globally Linearly Convergent Algorithm for the Computation of Wasserstein Barycenters

no code implementations12 Sep 2018 Lei Yang, Jia Li, Defeng Sun, Kim-Chuan Toh

When the support points of the barycenter are pre-specified, this problem can be modeled as a linear programming (LP) problem whose size can be extremely large.

Efficient sparse semismooth Newton methods for the clustered lasso problem

no code implementations22 Aug 2018 Meixia Lin, Yong-Jin Liu, Defeng Sun, Kim-Chuan Toh

Based on the new formulation, we derive an efficient procedure for its computation.

An Efficient Semismooth Newton Based Algorithm for Convex Clustering

no code implementations ICML 2018 Yancheng Yuan, Defeng Sun, Kim-Chuan Toh

Clustering may be the most fundamental problem in unsupervised learning which is still active in machine learning research because its importance in many applications.

A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients

no code implementations13 Oct 2012 Weimin Miao, Shaohua Pan, Defeng Sun

To seek a solution of high recovery quality beyond the reach of the nuclear norm, in this paper, we propose a rank-corrected procedure using a nuclear semi-norm to generate a new estimator.

Low-Rank Matrix Completion Quantum State Tomography

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