Results 1 - 10 of 22

Fast Stochastic Alternating Direction Method of Multipliers

by Leon Wenliang Zhong, James T. Kwok
"... We propose a new stochastic alternating direc-tion method of multipliers (ADMM) algorith-m, which incrementally approximates the ful-l gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, it im-proves the convergence rate ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
on convex problems fromO(1/√T) toO(1/T), where T is the num-ber of iterations. This matches the convergence rate of the batch ADMM algorithm, but without the need to visit all the samples in each itera-tion. Experiments on the graph-guided fused las-so demonstrate that the new algorithm is signif

Pathwise coordinate optimization

by Jerome Friedman, Trevor Hastie, Holger Höfling, Robert Tibshirani , 2007
"... We consider “one-at-a-time ” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in the lterature, but it seems to have been largely ignored. Indeed, it seems that coordinate-wise algorith ..."
Abstract - Cited by 325 (17 self) - Add to MetaCart
not work in the “fused lasso ” however, so we derive a generalized algorithm that yields the solution in much less time that a standard convex optimizer. Finally we generalize the procedure to the two-dimensional fused lasso, and demonstrate its performance on some image smoothing problems.

Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso

by Xi Chen, Seyoung Kim, Qihang Lin, Jaime G. Carbonell, Eric P. Xing , 1005
"... We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as ℓ1/ℓ2-regularized ..."
Abstract - Cited by 16 (3 self) - Add to MetaCart
multi-task regression assume that all of the output variables are equally related to the inputs, although in many real-world problems, outputs are related in a complex manner. In this paper, we propose graph-guided fused lasso (GFlasso) for structured multi-task regression that exploits the graph

Giannakis, “Distributed sparse linear regression

by Gonzalo Mateos, Student Member, Juan Andrés Bazerque, Student Member, Georgios B. Giannakis - IEEE Trans. Signal Process , 2010
"... Abstract—The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems. This paper develops algorithms to estimate the regression coefficients via Lasso when the training data are di ..."
Abstract - Cited by 46 (8 self) - Add to MetaCart
-frequency power spectrum density. Attaining different tradeoffs between complexity and convergence speed, three novel algorithms are obtained after reformulating the Lasso into a separable form, which is iteratively minimized using the alternating-direction method of multipliers so as to gain the desired degree

A multivariate regression approach to association analysis of a quantitative trait network

by Seyoung Kim, Kyung-ah Sohn, Eric P. Xing - Bioinformatics , 2009
"... Motivation: Many complex disease syndromes such as asthma con-sist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variat ..."
Abstract - Cited by 31 (4 self) - Add to MetaCart
variation may influence a group of highly correlated traits jointly, most of the previous association analyses con-sidered each phenotype separately, or combined results from a set of single-phenotype analyses. Results: We propose a new statistical framework called graph-guided fused lasso (GFlasso

FUSION OF THERMAL INFRARED HYPERSPECTRAL AND VIS RGB DATA USING GUIDED FILTER AND SUPERVISED FUSION GRAPH

by Wenzhi Liao, Frieke Van Coillie, Sidharta Gautama, Wilfried Philips
"... Nowadays, advanced technology in remote sensing allows us to get multi-sensor and multi-resolution data from the same region. Fusion of these data sources for classification remains challenging problems. We proposed a novel framework for fusion of low spatial-resolution Thermal Infrared (TI) hyper-s ..."
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-logical features generated on RGB data) through supervised fusion graph. Finally, the fused features are used by SVM classifier to generate the final classification map. Experimen-tal results on the classification of fusing real TI HS and RGB images demonstrate the effectiveness of the proposed method both

Association Analysis of Quantitative Trait Network

by Seyoung Kim, Kyung-ah Sohn, Eric P. Xing, Seyoung Kim, Kyung-ah Sohn, Eric P. Xing , 2008
"... Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical f ..."
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framework called graph-guided fused lasso (GFlasso) to address this issue in a principled way. Our approach explicitly represents the dependency structure among the quantitative traits as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over

Feature Grouping and Selection Over an Undirected Graph

by Sen Yang, Lei Yuan, Ying-cheng Lai, Xiaotong Shen, Peter Wonka, Jieping Ye
"... High-dimensional regression/classification continues to be an important and challenging problem, especially when featuresarehighlycorrelated. Featureselection,combinedwith additional structure information on the features has been considered to be promising in promoting regression/classification perf ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graphstructures. However, theformulationinGFlassorelies on pairwise sample correlations to perform feature grouping, which could introduce

Minimizing Nonconvex Non-Separable Functions

by Yaoliang Yu, Xun Zheng, Micol Marchetti-bowick, Eric P. Xing
"... Regularization has played a key role in de-riving sensible estimators in high dimensional statistical inference. A substantial amount of recent works has argued for nonconvex reg-ularizers in favor of their superior theoreti-cal properties and excellent practical perfor-mances. In a different but an ..."
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-algorithm by rigorously extending the proximal aver-age to the nonconvex setting. We formally prove its nice convergence properties, and il-lustrate its effectiveness on two applications: multi-task graph-guided fused lasso and ro-bust support vector machines. Experiments demonstrate that our method compares fa

Submitted to The American Journal of Human Genetics A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network

by Seyoung Kim, Kyung-ah Sohn, Eric P. Xing , 811
"... Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical f ..."
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framework called graph-guided fused lasso (GFlasso) to address this issue in a principled way. Our approach explicitly represents the dependency structure among the quantitative traits as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over
Results 1 - 10 of 22