Average Competitive Learning Vector Quantization
type: Dataset
description: We propose a new algorithm for vector quantization:Average Competitive Learning Vector Quantization(ACLVQ). It is a rather simple modi cation of the classical Competitive Learning Vector Quantization(CLVQ). This new formulation gives us similar results for the quantization error to those obtained by the CLVQ and reduce considerably the computation time to achieve the optimal quantizer. We establish the convergence of the method via the Kushner-Clark approach, and compare the two algorithms via the central limit Theorem. A simulation study is carried out showing the good performance of our proposal.
issued: 2026-05-22T11:35:36.994438
language: en
modified: 2026-05-22T11:35:36.994443
contact point: Organization
  fn: CCSD
keyword: 94a29-62l20-60f15-90c51-60f05-65d30-1
keyword: asymptotic-convergence
keyword: infoeu-reposemanticspreprint
keyword: kushner-clark-method
keyword: mathmath-stmathematics-mathstatistics-mathst
keyword: optimal-quantization
keyword: preprints-working-papers-
keyword: statthstatistics-statstatistics-theory-statth
keyword: stochastic-algorithms
landing page: https://hal.science/hal-00685960

Average Competitive Learning Vector Quantization
type: Distribution
format: HTML
issued: 2026-05-22T11:35:37.048338
modified: 2026-05-22T11:35:36.962150
access URL: https://hal.science/hal-00685960

test_moissonnage_selune
type: Agent