An aggregate and iterative disaggregate algorithm with proven optimality in machine learning

Young Woong Park*, Diego Klabjan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common machine learning problems such as the least absolute deviation regression problem, support vector machines, and semi-supervised support vector machines. We derive model-specific data aggregation and disaggregation procedures. We also show optimality, convergence, and the optimality gap of the approximated solution in each iteration. A computational study is provided.

Original languageEnglish (US)
Pages (from-to)199-232
Number of pages34
JournalMachine Learning
Volume105
Issue number2
DOIs
StatePublished - Nov 1 2016

Keywords

  • AID
  • Aggregate and iterative disaggregate
  • Data aggregation
  • Least absolute deviation regression
  • Machine learning
  • Optimization
  • Semi-supervised support vector machine
  • Support vector machine

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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