Gaussian bandwidth selection for manifold learning and classification

Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

ملخص

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.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)1676-1712
عدد الصفحات37
دوريةData Mining and Knowledge Discovery
مستوى الصوت34
رقم الإصدار6
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1 نوفمبر 2020

All Science Journal Classification (ASJC) codes

  • !!Information Systems
  • !!Computer Networks and Communications
  • !!Computer Science Applications

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