Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84787
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTsai, Flora S.en
dc.date.accessioned2013-07-10T06:28:43Zen
dc.date.accessioned2019-12-06T15:51:09Z-
dc.date.available2013-07-10T06:28:43Zen
dc.date.available2019-12-06T15:51:09Z-
dc.date.copyright2011en
dc.date.issued2011en
dc.identifier.urihttps://hdl.handle.net/10356/84787-
dc.identifier.urihttp://hdl.handle.net/10220/11109en
dc.description.abstractData visualization of high-dimensional data is possible through the use of dimensionality reduction techniques. However, in deciding which dimensionality reduction techniques to use in practice, quantitative metrics are necessary for evaluating the results of the transformation and visualization of the lower dimensional embedding. In this paper, we propose a manifold visualization metric based on the pairwise correlation of the geodesic distance in a data manifold. This metric is compared with other metrics based on the Euclidean distance, Mahalanobis distance, City Block metric, Minkowski metric, cosine distance, Chebychev distance, and Spearman distance. The results of applying different dimensionality reduction techniques on various types of nonlinear manifolds are compared and discussed. Our experiments show that our proposed metric is suitable for quantitatively evaluating the results of the dimensionality reduction techniques if the data lies on an open planar nonlinear manifold. This has practical significance in the implementation of knowledge-based visualization systems and the application of knowledge-based dimensionality reduction methods.en
dc.language.isoenen
dc.relation.ispartofseriesExpert systems with applicationsen
dc.rights© 2011 Elsevier Ltd.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleA visualization metric for dimensionality reductionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1016/j.eswa.2011.08.080en
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:EEE Journal Articles

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.