Work

Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing

Year: 2015

Type: article

Abstract: For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of features greatly exceeds the number of training examples, but where only a few features suffice for accurate classification. We show that sum-product GAMP can be used to (approximately) minimize the classification error rate and max-sum GAMP can be used to minimize a wide variety of regularized loss functions. Furthermore, we describe an expectation-maximization (EM)-based scheme to learn the associated model parameters online, as an alternative to cross-validation, and we show that GAMP's state-evolution framework can be used to accurately predict the misclassification rate. Finally, we present a detailed numerical study to confirm the accuracy, speed, and flexibility afforded by our GAMP-based approaches to binary linear classification and feature selection.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2015.2407311

Authors: Justin Ziniel, Philip Schniter, Per Sederberg

Institution: The Ohio State University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Office of Naval Research, Division of Computing and Communication Foundations

Awards: G3809904081, G3844814913, G6660321836