Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/95081
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Informatika / Computer science (N009)
Author(s): Karbauskaitė, Rasa;Kurasova, Olga;Dzemyda, Gintautas
Title: Selection of the number of neighbours of each data point for the locally linear embedding algorithm
Is part of: Information technology and control = Informacinės technologijos ir valdymas. , Vol. 36, no. 4 (2007)
Extent: p. 359-364
Date: 2007
Keywords: Locally linear embedding;Dimensionality reduction;Manifold learning
Abstract: This paper deals with a method, called locally linear embedding. It is a nonlinear dimensionality reduction technique that computes low-dimensional, neighbourhood preserving embeddings of high dimensional data and attempts to discover nonlinear structure in high dimensional data. The implementation of the algorithm is fairly straightforward, as the algorithm has only two control parameters: the number of neighbours of each data point and the regularisation parameter. The mapping quality is quite sensitive to these parameters. In this paper, we propose a new way for selecting the number of the nearest neighbours of each data point. Our approach is experimentally verified on two data sets: artificial data and real world pictures
Internet: http://itc.ktu.lt/itc364/Karbausk364.pdf
http://itc.ktu.lt/itc364/Karbausk364.pdf
Affiliation(s): Matematikos ir informatikos institutas
Vilniaus pedagoginis universitetas
Vytauto Didžiojo universitetas
Švietimo akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml7.46 kBXMLView/Open

MARC21 XML metadata

Show full item record

Page view(s)

8
checked on Sep 5, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.