DALASS: Variable selection in discriminant analysis via the LASSO

Authors:
Nickolay T. Trendafilov;Ian T. Jolliffe
Affiliations:
Department of Statistics, The Open University, Milton Keynes MK7 6AA, UK;Department of Meteorology, University of Reading, Reading RG6 6BB, UK
Venue:
Computational Statistics & Data Analysis
Year:
2007

Citing 2
Cited 3

Concept decompositions for large sparse text data using clustering

Machine Learning
Projected gradient approach to the numerical solution of the SCoTLASS

Computational Statistics & Data Analysis

Simple and interpretable discrimination

Computational Statistics & Data Analysis
Exact and approximate algorithms for variable selection in linear discriminant analysis

Computational Statistics & Data Analysis
Simultaneous model-based clustering and visualization in the Fisher discriminative subspace

Statistics and Computing

Quantified Score

Hi-index	0.03

Visualization

Abstract

The objective of DALASS is to simplify the interpretation of Fisher's discriminant function coefficients. The DALASS problem-discriminant analysis (DA) modified so that the canonical variates satisfy the LASSO constraint-is formulated as a dynamical system on the unit sphere. Both standard and orthogonal canonical variates are considered. The globally convergent continuous-time algorithms are illustrated numerically and applied to some well-known data sets.