Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/5761
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gao, Junbin | en |
dc.contributor.author | Kwan, Paul H | en |
dc.contributor.author | Guo, Yi | en |
dc.date.accessioned | 2010-05-06T10:08:00Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Neurocomputing, 72(4-6), p. 1242-1249 | en |
dc.identifier.issn | 1872-8286 | en |
dc.identifier.issn | 0925-2312 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/5761 | - |
dc.description.abstract | Further to our recent work on the robust L1 PCA we introduce a new version of robust PCA model based on the so-called multivariate Laplace distribution (called L1 distribution) proposed in Eltoft et al. [2006. On the multivariate Laplace distribution. IEEE Signal Process. Lett. 13(5), 300–303]. Due to the heavy tail and high component dependency characteristics of the multivariate L1 distribution, the proposed model is expected to be more robust against data outliers and fitting component dependency. Additionally, we demonstrate how a variational approximation scheme enables effective inference of key parameters in the probabilistic multivariate L1-PCA model. By doing so, a tractable Bayesian inference can be achieved based on the variational EM-type algorithm. | en |
dc.language | en | en |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
dc.relation.ispartof | Neurocomputing | en |
dc.title | Robust multivariate L1 principal component analysis and dimensionality reduction | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1016/j.neucom.2008.01.027 | en |
dc.subject.keywords | Pattern Recognition and Data Mining | en |
dc.subject.keywords | Neural, Evolutionary and Fuzzy Computation | en |
local.contributor.firstname | Junbin | en |
local.contributor.firstname | Paul H | en |
local.contributor.firstname | Yi | en |
local.subject.for2008 | 080109 Pattern Recognition and Data Mining | en |
local.subject.for2008 | 080108 Neural, Evolutionary and Fuzzy Computation | en |
local.subject.seo2008 | 890202 Application Tools and System Utilities | en |
local.profile.school | School of Science and Technology | en |
local.profile.school | Computer Science | en |
local.profile.email | wkwan2@une.edu.au | en |
local.output.category | C1 | en |
local.record.place | au | en |
local.record.institution | University of New England | en |
local.identifier.epublicationsrecord | une-20100414-152819 | en |
local.publisher.place | Netherlands | en |
local.format.startpage | 1242 | en |
local.format.endpage | 1249 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 72 | en |
local.identifier.issue | 4-6 | en |
local.contributor.lastname | Gao | en |
local.contributor.lastname | Kwan | en |
local.contributor.lastname | Guo | en |
dc.identifier.staff | une-id:wkwan2 | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.profile.role | author | en |
local.identifier.unepublicationid | une:5902 | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Robust multivariate L1 principal component analysis and dimensionality reduction | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Gao, Junbin | en |
local.search.author | Kwan, Paul H | en |
local.search.author | Guo, Yi | en |
local.uneassociation | Unknown | en |
local.identifier.wosid | 000263372000059 | en |
local.year.published | 2009 | en |
Appears in Collections: | Journal Article |
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