Exploratory basis pursuit classification

Authors:
M. Brown;N. P. Costen
Affiliations:
Control Systems Centre, School of Electrical and Electronics Engineering, University of Manchester, P.O. Box 88, Manchester M60 1QD, UK;Department Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
Venue:
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Year:
2005

Citing 3
Cited 2

Atomic Decomposition by Basis Pursuit

SIAM Review
Feature Selection via Concave Minimization and Support Vector Machines

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A robust nonlinear identification algorithm using PRESS statistic and forward regression

IEEE Transactions on Neural Networks

Rank related properties for Basis Pursuit and total variation regularization

Signal Processing
A Predual Proximal Point Algorithm Solving a Non Negative Basis Pursuit Denoising Model

International Journal of Computer Vision

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Abstract

Feature selection is a fundamental process in many classifier design problems. However, it is NP-complete and approximate approaches often require requires extensive exploration and evaluation. This paper describes a novel approach that represents feature selection as a continuous regularization problem which has a single, global minimum, where the model's complexity is measured using a 1-norm on the parameter vector. A new exploratory design process is also described that allows the designer to efficiently construct the complete locus of sparse, kernel-based classifiers. It allows the designer to investigate the optimal parameters' trajectories as the regularization parameter is altered and look for effects, such as Simpson's paradox, that occur in many multivariate data analysis problems. The approach is demonstrated on the well-known Australian Credit data set.