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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Journal ArticleDOI
Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015
Eva Malacova,Eva Malacova,Sawitchaya Tippaya,Helen D. Bailey,Kevin Chai,Brad M. Farrant,Amanuel Tesfay Gebremedhin,Helen Leonard,Michael Luke Marinovich,Natasha Nassar,Aloke Phatak,Camille Raynes-Greenow,Annette K. Regan,Annette K. Regan,Annette K. Regan,Antonia W. Shand,Antonia W. Shand,Carrington C. J. Shepherd,Carrington C. J. Shepherd,Ravisha Srinivasjois,Ravisha Srinivasjois,Gizachew Assefa Tessema,Gavin Pereira,Gavin Pereira,Gavin Pereira +24 more
TL;DR: Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history, and ensemble classifiers offered marginal improvement for prediction compared to logistic regression.
Proceedings ArticleDOI
An improved GLMNET for l1-regularized logistic regression
TL;DR: An improved GLMNET is proposed to address some theoretical and implementation issues and is found to be more efficient than a state-of-the-art coordinate descent method.
Journal ArticleDOI
An active learning approach for rapid characterization of endothelial cells in human tumors
Raghav Padmanabhan,Vinay H. Somasundar,Sandra D. Griffith,Jianliang Zhu,Drew Samoyedny,Kay See Tan,Jiahao Hu,Xuejun Liao,Lawrence Carin,Sam S. Yoon,Keith T. Flaherty,Robert S. DiPaola,Daniel F. Heitjan,Priti Lal,Michael Feldman,Badrinath Roysam,William M. F. Lee +16 more
TL;DR: FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials and supports a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.
Journal ArticleDOI
Sparse Kernel Logistic Regression Based On L 1=2 Regularization
TL;DR: A novel sparse version of KLR is proposed, the 1/2 quasi-norm kernel logistic regression (1/2-KLR), which integrates advantages of K LR and L1/1 regularization, and defines an efficient implementation scheme of sparse KLR.
Maximum entropy density estimation and modeling geographic distributions of species
TL;DR: This dissertation proposes a unified treatment for a large and general class of smoothing techniques that addresses the problem of geographic distributions of species in ecology and conservation in a statistically sound manner and allows principled extensions to situations when data collection is biased or when the authors have access to data on many related species.
References
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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.
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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.
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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.
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