title: Setting lower bounds on Jensen—Shannon divergence and its application to nearest neighbor document search
metadata.name: dc.contributor.author
metadata.value: Dobrynin, Vladimir Yu.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Rooney, Niall
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Serdyuk, Julian A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2019-01-16T15:55:59Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2019-01-16T15:55:59Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2018-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Dobrynin V. Yu., Rooney N., Serdyuk J. A. Setting lower bounds on Jensen—Shannon divergence and its application to nearest neighbor document search. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2018, vol. 14, iss. 4, pp. 334–345.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11702/spbu10.2018.406
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/15246
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The Jensen—Shannon divergence provides a mechanism to determine nearest neighbours in
  a document collection to a specific query document. This is an effective mechanism however
  for exhaustive search this can be a time-consuming process. In this paper, we show by setting
  lower bounds on the Jensen—Shannon divergence search we can reduce by up to a factor of
  60% the level of calculation for exhaustive search and 98% for approximate search, based on
  the nearest neighbour search in a real-world document collection. In these experiments a document
  corpus that contains 1 854 654 articles published in New York Times from 1987-01-01
  till 2007-06-19 (The New York Times Annotated Corpus) was used. As queries, 100 documents
  from same document corpus were selected randomly. We assess the effect on
  performance based on the reduction in the number of log function calculations. Approximate
  nearest neighbour search is based on clustering of documents using Contextual Document
  Clustering algorithm. We perform an approximated nearest neighbour search by finding the
  best matching set of cluster attractors to a query and limiting the search for documents to
  the attractors’ corresponding clusters.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 14; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: Jensen—Shannon divergence
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: nearest neighbors search
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: dimensionality reduction
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Setting lower bounds on Jensen—Shannon divergence and its application to nearest neighbor document search
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 06-Dobrynin.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/15246/1/06-Dobrynin.pdf
resources.format: Adobe PDF
resources.size: 311,25 kB