Stretchy binary classification

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

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)74-91
Number of pages18
JournalNeural Networks
Volume97
DOIs
Publication statusPublished - 2018 Jan 1

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Analysis of Variance

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

Cite this

Toh, Kar Ann ; Lin, Zhiping ; Sun, Lei ; Li, Zhengguo. / Stretchy binary classification. In: Neural Networks. 2018 ; Vol. 97. pp. 74-91.
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Stretchy binary classification. / Toh, Kar Ann; Lin, Zhiping; Sun, Lei; Li, Zhengguo.

In: Neural Networks, Vol. 97, 01.01.2018, p. 74-91.

Research output: Contribution to journalArticle

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