Open AccessProceedings Article
EfficientL 1 regularized logistic regression
Sun-In Lee,Honglak Lee,Pieter Abbeel,Andrew Y. Ng +3 more
- pp 401-408
Reads0
Chats0
TLDR
Theoretical results show that the proposed efficient algorithm for L1 regularized logistic regression is guaranteed to converge to the global optimum, and experiments show that it significantly outperforms standard algorithms for solving convex optimization problems.Abstract:
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many practical settings. In this paper, we propose an efficient algorithm for L1 regularized logistic regression. Our algorithm iteratively approximates the objective function by a quadratic approximation at the current point, while maintaining the L1 constraint. In each iteration, it uses the efficient LARS (Least Angle Regression) algorithm to solve the resulting L1 constrained quadratic optimization problem. Our theoretical results show that our algorithm is guaranteed to converge to the global optimum. Our experiments show that our algorithm significantly outperforms standard algorithms for solving convex optimization problems. Moreover, our algorithm outperforms four previously published algorithms that were specifically designed to solve the L1 regularized logistic regression problem.read more
Citations
More filters
Posted Content
Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization
Chen Chen,Jaewoo Lee +1 more
TL;DR: In this paper, Wang et al. proposed two stochastic alternating direction method of multipliers (ADMM) algorithms, namely ssADMM and mpADMM, to minimize composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as $L_1$ norm or nuclear norm.
Journal ArticleDOI
Identification of Encrypted Data Stream Based on Sparse Randomness Features and GMM
TL;DR: This paper proposed Gaussian mixture model using sparse feature selection of randomness to solve the identification of encrypted data stream and shows that the average identification rate is over 90%.
Proceedings ArticleDOI
Curator - A system for creating data sets for behavioral malware detection
TL;DR: Curator as discussed by the authors is a distributed system for detecting malware during execution using machine learning models, which is based on Naive Bayes, Logistic Regression, and Random Forests.
Journal ArticleDOI
Group LARS-Based Iterative Reweighted Least Squares Methodology for Efficient Statistical Modeling of Memory Designs
TL;DR: In this article , a group LARS-based approach is proposed to handle groups of variables and exploits the natural evolution of the solution to speed up the search for the critical features of the classifier.
Proceedings ArticleDOI
Stochastic Mirror Descent Algorithm for L1-Regularized Risk Minimizations
Hua Ouyang,Alexander G. Gray +1 more
TL;DR: Experiments on large-scale datasets demonstrate that the proposed SMD algorithm is much faster than the recently proposed truncated gradient algorithm (TG), and has a higher testing accuracy.
References
More filters
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI
Generalized Linear Models
TL;DR: This is the rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
Related Papers (5)
Least angle regression
Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more