Stretchy binary classification

Kar Ann Toh; Zhiping Lin; Lei Sun; Zhengguo Li

doi:10.1016/j.neunet.2017.09.015

Stretchy binary classification

Kar Ann Toh, Zhiping Lin, Lei Sun, Zhengguo Li

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	74-91
Number of pages	18
Journal	Neural Networks
Volume	97
DOIs	https://doi.org/10.1016/j.neunet.2017.09.015
Publication status	Published - 2018 Jan

Bibliographical note

Funding Information:
The authors are thankful to the editor and reviewers for their constructive comments and Dr. Bernd Burgstaller for proof reading the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: NRF-2015R1D1A1A09061316 ).

Publisher Copyright:
© 2017 Elsevier Ltd

All Science Journal Classification (ASJC) codes

Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neunet.2017.09.015

Cite this

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title = "Stretchy binary classification",

abstract = "In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least ℓp-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.",

author = "Toh, {Kar Ann} and Zhiping Lin and Lei Sun and Zhengguo Li",

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doi = "10.1016/j.neunet.2017.09.015",

language = "English",

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AU - Toh, Kar Ann

AU - Lin, Zhiping

AU - Sun, Lei

AU - Li, Zhengguo

N1 - Funding Information: The authors are thankful to the editor and reviewers for their constructive comments and Dr. Bernd Burgstaller for proof reading the manuscript. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: NRF-2015R1D1A1A09061316 ). Publisher Copyright: © 2017 Elsevier Ltd

PY - 2018/1

Y1 - 2018/1

N2 - In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least ℓp-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.

AB - In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least ℓp-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.

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JO - Neural Networks

JF - Neural Networks

ER -

Stretchy binary classification

Abstract

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