Linked Life Data

11125150

Source:http://linkedlifedata.com/resource/pubmed/id/11125150

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5500
pubmed:dateCreated
2000-12-28
pubmed:abstractText
Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0036-8075
pubmed:author
pubmed:issnType
Print
pubmed:day
22
pubmed:volume
290
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2323-6
pubmed:dateRevised
2007-3-19
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Nonlinear dimensionality reduction by locally linear embedding.
pubmed:affiliation
Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, UK. roweis@gatsby.ucl.ac.uk
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't