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Updated: Jun 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Robust classifiers for data reduced via random projections.

Angshul Majumdar1, Rabab K Ward

  • 1Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada. angshulm@ece.ubc.ca

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 29, 2010
PubMed
Summary
This summary is machine-generated.

Random projection (RP) offers a data-independent approach to dimensionality reduction, crucial for high-dimensional data like images. This study confirms RP

Related Experiment Videos

Last Updated: Jun 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Science

Background:

  • High-dimensional data, common in image classification, increases computational cost for algorithms.
  • Traditional dimensionality reduction methods are data-dependent, causing practical issues.
  • Random projection (RP) is a data-independent alternative for dimensionality reduction.

Purpose of the Study:

  • To evaluate the robustness of Random Projection (RP) for dimensionality reduction.
  • To assess the performance of recently proposed classifiers with RP.
  • To provide theoretical and experimental validation of RP's effectiveness.

Main Methods:

  • Utilized Random Projection (RP) for data-independent dimensionality reduction.
  • Investigated the robustness of Sparse Classifier (SC), Group SC, and Nearest Subspace Classifier with RP.
  • Conducted theoretical analyses and experimental evaluations.

Main Results:

  • Demonstrated the robustness of SC, Group SC, and Nearest Subspace Classifier to RP.
  • Confirmed theoretical findings through empirical validation.
  • Showcased the effectiveness of RP in conjunction with advanced classifiers.

Conclusions:

  • Random Projection is a robust dimensionality reduction technique suitable for various classifiers.
  • RP effectively handles high-dimensional data, improving classification efficiency.
  • The study validates RP's utility in modern machine learning applications.