Space and time efficient kernel density estimation in high dimensions
Name
NeurIPS-2019-space-and-time-efficient-kernel-density-estimation-in-high-dimensions-Paper.pdf
Description
Published version
Size
693.32 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Indyk, Piotr
Wagner, Tal
Date Issued
December 2019
Journal
Advances in Neural Information Processing Systems
Publisher
Morgan Kaufmann Publishers
Citation
Backurs, Arturs et al. “Space and time efficient kernel density estimation in high dimensions.” Advances in Neural Information Processing Systems, 32 (December 2019) © 2019 The Author(s)
Version
Final published version
Abstract
Recently, Charikar and Siminelakis (2017) presented a framework for kernel density estimation in provably sublinear query time, for kernels that possess a certain hashing-based property. However, their data structure requires a significantly increased super-linear storage space, as well as super-linear preprocessing time. These limitations inhibit the practical applicability of their approach on large datasets. In this work, we present an improvement to their framework that retains the same query time, while requiring only linear space and linear preprocessing time. We instantiate our framework with the Laplacian and Exponential kernels, two popular kernels which possess the aforementioned property. Our experiments on various datasets verify that our approach attains accuracy and query time similar to Charikar and Siminelakis (2017), with significantly improved space and preprocessing time.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2019/hash/a2ce8f1706e52936dfad516c23904e3e-Abstract.html
