Open AccessProceedings Article
EfficientL 1 regularized logistic regression
Sun-In Lee,Honglak Lee,Pieter Abbeel,Andrew Y. Ng +3 more
- pp 401-408
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
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Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression
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Inexact primal–dual gradient projection methods for nonlinear optimization on convex set
TL;DR: In this paper, a primal-dual inexact gradient projection method for nonlinear optimization problems with convex-set constraint is proposed, which only needs inexact computation of the pr...
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A Non-Invasive Flexible Glucose Monitoring Sensor Using a Broadband Reject Filter
Moussa Bteich,Jessica Hanna,Joseph Constantine,Rouwaida Kanj,Youssef Tawk,Ali Ramadan,Assaad A. Eid +6 more
TL;DR: In this article, a flexible broadband reject filter was proposed to track the variations of the glucose level across the 1.25-2.65 GHz frequency span. But, the proposed broadband reject response is achieved by relying on a modified log-periodic distribution of the open loop resonators.
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Alzheimer-type dementia prediction by sparse logistic regression using claim data.
TL;DR: The results indicate that SLR-L1 tended to include less useful features, whereas SLr-L0 narrowed down influential features, which might be more useful than SLR -L1 for practical use or the discussion of risk factors with medical experts.
Book ChapterDOI
Multiplicative updates for L 1 -regularized linear and logistic regression
TL;DR: This paper shows how to derive multiplicative updates for problems in L1-regularized linear and logistic regression and describes efficient implementations for large-scale problems of interest.
References
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