Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset

T Bouwmans, A Sobral, S Javed, SK Jung… - Computer Science …, 2017 - Elsevier
Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

Structure learning in graphical modeling

M Drton, MH Maathuis - Annual Review of Statistics and Its …, 2017 - annualreviews.org
A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

Proximal algorithms

N Parikh, S Boyd - Foundations and trends® in Optimization, 2014 - nowpublishers.com
This monograph is about a class of optimization algorithms called proximal algorithms. Much
like Newton's method is a standard tool for solving unconstrained smooth optimization …

Robust low-rank tensor recovery: Models and algorithms

D Goldfarb, Z Qin - SIAM Journal on Matrix Analysis and Applications, 2014 - SIAM
Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for
multilinear data analysis against outliers, gross corruptions, and missing values and has a …

Latent variable graphical model selection via convex optimization

V Chandrasekaran, PA Parrilo… - 2010 48th Annual …, 2010 - ieeexplore.ieee.org
Suppose we have samples of a subset of a collection of random variables. No additional
information is provided about the number of latent variables, nor of the relationship between …

[PDF][PDF] Node-based learning of multiple Gaussian graphical models

K Mohan, P London, M Fazel, D Witten… - The Journal of Machine …, 2014 - jmlr.org
We consider the problem of estimating high-dimensional Gaussian graphical models
corresponding to a single set of variables under several distinct conditions. This problem is …

[PDF][PDF] Learning graphical models with hubs

KM Tan, P London, K Mohan, SI Lee, M Fazel… - arXiv preprint arXiv …, 2014 - jmlr.org
We consider the problem of learning a high-dimensional graphical model in which there are
a few hub nodes that are densely-connected to many other nodes. Many authors have …

[PDF][PDF] 正则化稀疏模型

刘建伟, 崔立鹏, 刘泽宇, 罗雄麟 - 计算机学报, 2015 - cjc.ict.ac.cn
摘要正则化稀疏模型在机器学习和图像处理等领域发挥着越来越重要的作用,
它具有变量选择功能, 可以解决建模中的过拟合等问题. Tibshirani 提出的Lasso …

Solving multiple-block separable convex minimization problems using two-block alternating direction method of multipliers

X Wang, M Hong, S Ma, ZQ Luo - arXiv preprint arXiv:1308.5294, 2013 - arxiv.org
In this paper, we consider solving multiple-block separable convex minimization problems
using alternating direction method of multipliers (ADMM). Motivated by the fact that the …

Alternating proximal gradient method for convex minimization

S Ma - Journal of Scientific Computing, 2016 - Springer
In this paper, we apply the idea of alternating proximal gradient to solve separable convex
minimization problems with three or more blocks of variables linked by some linear …