A comparison of dimensionality reduction methods using topology preservation indexes

Página do item simplificado
dc.contributor.authorDe Medeiros C.J.F.
dc.contributor.authorCosta J.A.F.
dc.contributor.authorSilva L.A.
dc.date.accessioned2024-03-13T01:10:29Z
dc.date.available2024-03-13T01:10:29Z
dc.date.issued2011
dc.description.abstractDue to the remarkable technological developments experienced in recent decades, the vast amount of data had created new opportunities and challenges in the field of knowledge discovery and data mining. Factors like size and high dimensionality of databases adds difficulties to the complex task of discovering patterns hidden in masses of data. The feasibility of highdimensional data exploration depends on techniques known as dimensionality reduction methods. When class labels are available, an optimization function can be used to maximize intra class cohesion and inter class separation. However, in many practical situations information about class is not available. This paper focuses on unsupervised dimensionality reduction techniques, an important phase in exploratory data analysis. Six important methods are described: Principal components analysis, Sammon projection, Auto-associative Neural network, Kohonen maps, Isomap and Locally Linear Embedding. Three quality indexes are proposed to try to quantify to some degree the topology preservation between input and output spaces. Comparisons are performed using benchmark data sets. Results and tests focused two-dimensional projections for data visualization purposes. © 2011 Springer-Verlag.
dc.description.firstpage437
dc.description.lastpage445
dc.description.volume6936 LNCS
dc.identifier.doi10.1007/978-3-642-23878-9_52
dc.identifier.issn0302-9743
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36944
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsAcesso Restrito
dc.subject.otherlanguagedata mining
dc.subject.otherlanguagedata visualization
dc.subject.otherlanguagedimensionality reduction
dc.subject.otherlanguageintelligent systems
dc.subject.otherlanguageneural networks
dc.subject.otherlanguageprojections
dc.subject.otherlanguageunsupervised methods
dc.titleA comparison of dimensionality reduction methods using topology preservation indexes
dc.typeArtigo de evento
local.scopus.citations3
local.scopus.eid2-s2.0-80053037488
local.scopus.subjectAutoassociative neural networks
local.scopus.subjectBenchmark data
local.scopus.subjectClass labels
local.scopus.subjectClass separation
local.scopus.subjectComplex task
local.scopus.subjectdimensionality reduction
local.scopus.subjectDimensionality reduction method
local.scopus.subjectDimensionality reduction techniques
local.scopus.subjectExploratory data analysis
local.scopus.subjectHigh dimensional data
local.scopus.subjectHigh dimensionality
local.scopus.subjectInput and outputs
local.scopus.subjectKnowledge discovery and data minings
local.scopus.subjectLocally linear embedding
local.scopus.subjectOptimization function
local.scopus.subjectPrincipal components analysis
local.scopus.subjectprojections
local.scopus.subjectQuality indices
local.scopus.subjectTechnological development
local.scopus.subjectTopology preservation
local.scopus.subjectTwo-dimensional projection
local.scopus.subjectUnsupervised method
local.scopus.subjectAutoassociative neural networks
local.scopus.subjectDimensionality reduction
local.scopus.subjectDimensionality reduction method
local.scopus.subjectDimensionality reduction techniques
local.scopus.subjectKnowledge discovery and data minings
local.scopus.subjectPrincipal components analysis
local.scopus.subjectProjections
local.scopus.subjectUnsupervised method
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80053037488&origin=inward
Arquivos
Coleções
Artigo de evento