A Direct Estimation Approach to Sparse Linear Discriminant Analysis 论文
2011Journal of the American Statistical Association引用 273
Sparse and Compressive Sensing TechniquesFace and Expression RecognitionGene expression and cancer classification
摘要
This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix and the difference of the mean vectors, we introduce a simple and effective classifier by estimating the product directly through constrained 1 minimization. The estimator can be implemented efficiently using linear programming and the resulting classifier is called the linear programming discriminant (LPD) rule.