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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Stretchy binary classification.

Kar-Ann Toh1, Zhiping Lin2, Lei Sun3

  • 1School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Republic of Korea.

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|November 3, 2017
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Summary
This summary is machine-generated.

This study presents a new analytic method for compressive binary classification, minimizing parameter vector norms under error constraints. The approach offers unbiased and biased estimations, with potential for sparse solutions in machine learning.

Keywords:
Parameter learningPattern classificationSparse estimation

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Area of Science:

  • Machine Learning
  • Statistical Learning Theory
  • Optimization

Background:

  • Compressive sensing and binary classification are crucial in data analysis.
  • Existing methods may lack analytical tractability or sparsity control.
  • The need for efficient parameter estimation under constraints is growing.

Purpose of the Study:

  • To introduce an analytic formulation for compressive binary classification.
  • To minimize the least ℓp-norm of the parameter vector subject to classification error.
  • To develop a stretchable estimation extending pseudoinverse methods.

Main Methods:

  • Formulating the problem as a constrained optimization task.
  • Developing analytic estimations based on left and right pseudoinverse constructions.
  • Conducting variance analysis to compare estimation properties.
  • Performing numerical investigations on synthetic and real-world datasets.

Main Results:

  • An analytic and stretchable estimation is proposed.
  • Left pseudoinverse estimation is unbiased; right pseudoinverse estimation is biased.
  • Sparseness is achievable for the biased estimation under specific conditions.
  • Numerical results validate the proposed methods.

Conclusions:

  • The proposed analytic formulation provides a novel approach to compressive binary classification.
  • The choice between left and right pseudoinverse estimations impacts bias and sparsity.
  • The method demonstrates effectiveness on diverse datasets, offering a valuable tool for machine learning practitioners.