dc.contributor.author | Indyk, Piotr | |
dc.contributor.author | Wagner, Tal | |
dc.date.accessioned | 2021-01-13T19:01:08Z | |
dc.date.available | 2021-01-13T19:01:08Z | |
dc.date.issued | 2019-12 | |
dc.identifier.issn | 1049-5258 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/129407 | |
dc.description.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. | en_US |
dc.description.sponsorship | National Science Foundation (U.S.). Transdisciplinary Research in Principles of Data Science (Award 1740751) | en_US |
dc.language.iso | en | |
dc.publisher | Morgan Kaufmann Publishers | en_US |
dc.relation.isversionof | https://papers.nips.cc/paper/2019/hash/a2ce8f1706e52936dfad516c23904e3e-Abstract.html | en_US |
dc.rights | 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. | en_US |
dc.source | Neural Information Processing Systems (NIPS) | en_US |
dc.title | Space and time efficient kernel density estimation in high dimensions | en_US |
dc.type | Article | en_US |
dc.identifier.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) | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.relation.journal | Advances in Neural Information Processing Systems | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dc.date.updated | 2020-12-18T16:35:13Z | |
dspace.orderedauthors | Backurs, A; Indyk, P; Wagner, T | en_US |
dspace.date.submission | 2020-12-18T16:35:16Z | |
mit.journal.volume | 32 | en_US |
mit.license | PUBLISHER_POLICY | |
mit.metadata.status | Complete | |