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Data Mining and Knowledge Discovery

Erschienen in:

02.07.2020

Gaussian bandwidth selection for manifold learning and classification

verfasst von: Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 6/2020

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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.

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Titel: Gaussian bandwidth selection for manifold learning and classification
verfasst von: Ofir Lindenbaum
Moshe Salhov
Arie Yeredor
Amir Averbuch
Publikationsdatum: 02.07.2020
Verlag: Springer US
Erschienen in: Data Mining and Knowledge Discovery / Ausgabe 6/2020
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-020-00692-x

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