Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset
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 …
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 …
correspond to variables of interest. The edges of the graph reflect allowed conditional …
Proximal algorithms
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 …
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 …
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 …
information is provided about the number of latent variables, nor of the relationship between …
[PDF][PDF] Node-based learning of multiple Gaussian graphical models
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 …
corresponding to a single set of variables under several distinct conditions. This problem is …
[PDF][PDF] Learning graphical models with hubs
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 …
a few hub nodes that are densely-connected to many other nodes. Many authors have …
Solving multiple-block separable convex minimization problems using two-block alternating direction method of multipliers
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 …
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 …
minimization problems with three or more blocks of variables linked by some linear …