Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863152 | Neural Networks | 2018 | 18 Pages |
Abstract
In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least âp-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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Kar-Ann Toh, Zhiping Lin, Lei Sun, Zhengguo Li,