Gaussian bandwidth selection for manifold learning and classification

Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

Research output: Contribution to journalArticlepeer-review

Abstract

Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s bandwidth, highly affects the performance of the task in hand. We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning. For manifold learning, we seek a scale which is best at capturing the manifold’s intrinsic dimension. For classification, we propose three methods for estimating the scale, which optimize the classification results in different senses. The proposed frameworks are simulated on artificial and on real datasets. The results show a high correlation between optimal classification rates and the estimated scales. Finally, we demonstrate the approach on a seismic event classification task.

Original languageEnglish
Pages (from-to)1676-1712
Number of pages37
JournalData Mining and Knowledge Discovery
Volume34
Issue number6
DOIs
StatePublished - 1 Nov 2020

Keywords

  • Classification
  • Diffusion maps
  • Dimensionality reduction
  • Kernel methods

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