Sparse Matrix-based Random Projection for Classification
type: Dataset
description: As a typical dimensionality reduction technique, random projection has been widely applied in a variety of fields concerning categorization. The construction of random projection has also been deeply studied, based on the principle of preserving the pairwise distances of a set of data projected from a high-dimensional space onto a low-dimensional subspace. Considering random projection is mainly exploited for the task of classification, this paper is novelly developed to study random projection from the viewpoint of feature selection, rather than of the traditional distance preservation. This yields a somewhat surprising result, that is, theoretically the sparsest random matrix with only one nonzero element in each column, can present better feature selection performance than other more dense matrices. Extensive experiments on binary classification also confirm the theoretical conjecture. Apparently, this result will be very attractive for dimensionality reduction due to its breakthrough on both complexity reduction and performance improvement.
issued: 2026-05-06T05:59:34.456967
language: en
modified: 2026-05-06T05:59:34.456972
contact point: Organization
  fn: CCSD
keyword: classi-cation
keyword: distance-preservation
keyword: feature-selection
keyword: infoeu-reposemanticspreprint
keyword: infoinfo-cvcomputer-science-cscomputer-vision-and-pattern-recognition-cscv
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: preprints-working-papers-
keyword: random-matrix
keyword: random-projection
keyword: sparsest
keyword: statmlstatistics-statmachine-learning-statml
landing page: https://hal.science/hal-00951936

Sparse Matrix-based Random Projection for Classification
type: Distribution
format: HTML
issued: 2026-05-06T05:59:34.474868
modified: 2026-05-06T05:59:34.445274
access URL: https://hal.science/hal-00951936

test_moissonnage_selune
type: Agent