Structured feature selection using coordinate descent optimization
نویسندگان
چکیده
منابع مشابه
Learning Structured Classifiers with Dual Coordinate Descent
We present a unified framework for online learning of structured classifiers. This framework handles a wide family of convex loss functions that includes as particular cases CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family; for the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, ...
متن کاملFeature Clustering for Accelerating Parallel Coordinate Descent
Large-scale `1-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. High-performance algorithms and implementations are critical to efficiently solving these problems. Building upon previous work on coordinate descent algorithms for `1-regularized probl...
متن کاملA unified approach to statistical tomography using coordinate descent optimization
Over the past years there has been considerable interest in statistically optimal reconstruction of cross-sectional images from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem, most existing reconstruction algorithms...
متن کاملStochastic Coordinate Descent for Nonsmooth Convex Optimization
Stochastic coordinate descent, due to its practicality and efficiency, is increasingly popular in machine learning and signal processing communities as it has proven successful in several large-scale optimization problems , such as l1 regularized regression, Support Vector Machine, to name a few. In this paper, we consider a composite problem where the nonsmoothness has a general structure that...
متن کاملPenalized Bregman Divergence Estimation via Coordinate Descent
Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2016
ISSN: 1471-2105
DOI: 10.1186/s12859-016-0954-4