Stretchy binary classification

Abstract

In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least ℓ^p-norm of the parameter vector subject to a classification error constraint. An analytic and stretchable estimation is conjectured where the estimation can be viewed as an extension of the pseudoinverse with left and right constructions. Our variance analysis indicates that the estimation based on the left pseudoinverse is unbiased and the estimation based on the right pseudoinverse is biased. Sparseness can be obtained for the biased estimation under certain mild conditions. The proposed estimation is investigated numerically using both synthetic and real-world data.

Keywords: Parameter learning; Pattern classification; Sparse estimation.