SparseNet: Coordinate Descent With Nonconvex Penalties
نویسندگان
چکیده
منابع مشابه
SparseNet: Coordinate Descent With Nonconvex Penalties.
We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with a variety of convex-relaxation algorithms for finding good solutions. In this article we pursue a coordinate-descent approach for optimization, and study its convergence properties. We characterize the properties of penalties suitable for...
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 کاملAccelerated Coordinate Descent with Adaptive Coordinate Frequencies
Coordinate descent (CD) algorithms have become the method of choice for solving a number of machine learning tasks. They are particularly popular for training linear models, including linear support vector machine classification, LASSO regression, and logistic regression. We propose an extension of the CD algorithm, called the adaptive coordinate frequencies (ACF) method. This modified CD schem...
متن کاملCoordinate Descent Algorithms for Nonconvex Penalized Regression, with Applications to Biological Feature Selection.
A number of variable selection methods have been proposed involving nonconvex penalty functions. These methods, which include the smoothly clipped absolute deviation (SCAD) penalty and the minimax concave penalty (MCP), have been demonstrated to have attractive theoretical properties, but model fitting is not a straightforward task, and the resulting solutions may be unstable. Here, we demonstr...
متن کاملCoordinate Descent Algorithms for Nonconvex Penalized Regression, with Applications to Biological Feature Selection By
A number of variable selection methods have been proposed involving nonconvex penalty functions. These methods, which include the smoothly clipped absolute deviation (SCAD) penalty and the minimax concave penalty (MCP), have been demonstrated to have attractive theoretical properties, but model fitting is not a straightforward task, and the resulting solutions may be unstable. Here, we demonstr...
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: Journal of the American Statistical Association
سال: 2011
ISSN: 0162-1459,1537-274X
DOI: 10.1198/jasa.2011.tm09738