Trust region Newton methods for large-scale logistic regression

Lin, Chih-Jen; Weng, Ruby C.; Keerthi, S. Sathiya

doi:10.1145/1273496.1273567

Cited by 264 publications

(277 citation statements)

References 14 publications

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“…Later, those histograms are categorized using a support vector machine. The SVM has been trained using logistic regression (LR-SVM) [19] with the LIBLINEAR software package [20]. Finally, the shown times results have been obtained using laptop with an Intel T7700 Core Duo CPU and 2Gb of RAM.…”

Section: Parameter Settingmentioning

confidence: 99%

Efficient Object Pixel-Level Categorization Using Bag of Features

Aldavert

Ramisa

Toledo

et al. 2009

Advances in Visual Computing

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Abstract. In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score of a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.

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Section: Parameter Settingmentioning

confidence: 99%

Efficient Object Pixel-Level Categorization Using Bag of Features

Aldavert

Ramisa

Toledo

et al. 2009

Advances in Visual Computing

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“…LiblieaR is an R interface to LIBLINEAR, a C/C++ library for large linear classification (Fan et al, 2008). LIBLINEAR not only has good theoretical properties, but also shows promising performance in practice (Fan et al, 2008;Hsieh et al, 2008;Keerthi et al, 2008;Lin et al, 2008). L2-regularized L2-loss support vector classification model (Fan et al, 2008) in LiblieaR was applied to construct the predictor.…”

Section: Predictor Construction and Evaluationmentioning

confidence: 99%

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

et al. 2011

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Single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), are responsible for most of human genetic diseases. Discriminate the deleterious SAPs from neutral ones can help identify the disease genes and understand the mechanism of diseases. In this work, a method of deleterious SAP prediction at system level was established. Unlike most existing methods, our method not only considers the sequence and structure information, but also the network information. The integration of network information can improve the performance of deleterious SAP prediction. To make our method available to the public, we developed SySAP (a System-level predictor of deleterious Single Amino acid Polymorphisms), an easy-to-use and high accurate web server. SySAP is freely available at http://www.biosino.org/ SySAP/and http://lifecenter.sgst.cn/SySAP/.

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“…They proposed a specialized algorithm that solve (3) to estimate the active set (the set of components w l that are zero at the minimizer of (39)) and use a Newton-like enhancement to the search direction using the projection of the Hessian of the log likelihood function onto the set of nonzero components w l . Lin et al [23] proposed a trust region Newton method to solve a large-scale 2 -regularized logistic regression problem of the form:…”

Section: Conclusion and Extensionsmentioning

confidence: 99%

A coordinate gradient descent method for ℓ 1-regularized convex minimization

Yun

Toh

2009

Comput Optim Appl

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In applications such as signal processing and statistics, many problems involve finding sparse solutions to under-determined linear systems of equations. These problems can be formulated as a structured nonsmooth optimization problems, i.e., the problem of minimizing 1 -regularized linear least squares problems. In this paper, we propose a block coordinate gradient descent method (abbreviated as CGD) to solve the more general 1 -regularized convex minimization problems, i.e., the problem of minimizing an 1 -regularized convex smooth function. We establish a Q-linear convergence rate for our method when the coordinate block is chosen by a Gauss-Southwell-type rule to ensure sufficient descent. We propose efficient implementations of the CGD method and report numerical results for solving large-scale 1 -regularized linear least squares problems arising in compressed sensing and image deconvolution as well as large-scale 1 -regularized logistic regression problems for feature selection in data classification. Comparison with several state-of-the-art algorithms specifically designed for solving large-scale 1 -regularized linear least squares or logistic regression problems suggests that an efficiently implemented CGD method may outperform these algorithms despite the fact that the CGD method is not specifically designed just to solve these special classes of problems.

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Trust region Newton methods for large-scale logistic regression

Cited by 264 publications

References 14 publications

Efficient Object Pixel-Level Categorization Using Bag of Features

Efficient Object Pixel-Level Categorization Using Bag of Features

SySAP: a system-level predictor of deleterious single amino acid polymorphisms

A coordinate gradient descent method for ℓ 1-regularized convex minimization

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