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Reports (Research Report) Year : 2010

Kernel Approximation by Locality Sensitive Hashing with Application to Active Learning

Abstract

Locality Sensitive Hashing (LSH) methods are being successfully employed for scaling similarity queries or similarity joins to large databases. We show that operations requiring intensive kernel computations can also benefit from the use of LSH. Specifically, this allows to accelerate active learning methods for which the query corresponds to a class boundary.
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Dates and versions

hal-01125750 , version 1 (06-03-2015)

Identifiers

  • HAL Id : hal-01125750 , version 1

Cite

Michel Crucianu. Kernel Approximation by Locality Sensitive Hashing with Application to Active Learning. [Research Report] CEDRIC-10-1898, CEDRIC Lab/CNAM. 2010. ⟨hal-01125750⟩
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