DeepAI AI Chat
Log In Sign Up

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

by   Zhiwei Qin, et al.
Lehigh University
Columbia University

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.


page 1

page 2

page 3

page 4


New methods for SVM feature selection

Support Vector Machines have been a popular topic for quite some time no...

Alternating direction method of multipliers for regularized multiclass support vector machines

The support vector machine (SVM) was originally designed for binary clas...

A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm

A large number of experimental data shows that Support Vector Machine (S...

A novel embedded min-max approach for feature selection in nonlinear SVM classification

In recent years, feature selection has become a challenging problem in s...

Identifying The Most Informative Features Using A Structurally Interacting Elastic Net

Feature selection can efficiently identify the most informative features...

Learning Using Privileged Information: SVM+ and Weighted SVM

Prior knowledge can be used to improve predictive performance of learnin...