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https://hdl.handle.net/1959.11/3492
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Guo, Yi | en |
dc.contributor.author | Gao, Junbin | en |
dc.contributor.author | Kwan, Paul Hing | en |
dc.date.accessioned | 2009-11-30T16:41:00Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | IEEE Transactions on a Pattern Analysis and Machine Intelligence, 30(8), p. 1490-1495 | en |
dc.identifier.issn | 0162-8828 | en |
dc.identifier.uri | https://hdl.handle.net/1959.11/3492 | - |
dc.description.abstract | In most existing dimensionality reduction algorithms, the main objective is to preserve relational structure among objects of the input space in a low dimensional embedding space. This is achieved by minimizing the inconsistency between two similarity/dissimilarity measures, one for the input data and the other for the embedded data, via a separate matching objective function. Based on this idea, a new dimensionality reduction method called twin kernel embedding (TKE) is proposed. TKE addresses the problem of visualizing non-vectorial data that is difficult for conventional methods in practice due to the lack of efficient vectorial representation. TKE solves this problem by minimizing the inconsistency between the similarity measures captured respectively by their kernel gram matrices in the two spaces. In the implementation, by optimizing a nonlinear objective function using the gradient descent algorithm, a local minimum can be reached. The results obtained include both the optimal similarity preserving embedding and the appropriate values for the hyperparameters of the kernel. Experimental evaluation on real non-vectorial datasets confirmed the effectiveness of TKE. TKE can be applied to other types of data beyond those mentioned in this paper whenever suitable measures of similarity/dissimilarity can be defined on the input data. | en |
dc.language | en | en |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en |
dc.relation.ispartof | IEEE Transactions on a Pattern Analysis and Machine Intelligence | en |
dc.title | Twin Kernel Embedding | en |
dc.type | Journal Article | en |
dc.identifier.doi | 10.1109/TPAMI.2008.74 | en |
dc.subject.keywords | Pattern Recognition and Data Mining | en |
local.contributor.firstname | Yi | en |
local.contributor.firstname | Junbin | en |
local.contributor.firstname | Paul Hing | en |
local.subject.for2008 | 080109 Pattern Recognition and Data Mining | en |
local.subject.seo2008 | 890299 Computer Software and Services not elsewhere classified | en |
local.profile.school | School of Science and Technology | en |
local.profile.school | School of Science and Technology | en |
local.profile.email | yguo4@une.edu.au | en |
local.profile.email | jgao@une.edu.au | 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 | pes:6774 | en |
local.publisher.place | United States of America | en |
local.format.startpage | 1490 | en |
local.format.endpage | 1495 | en |
local.peerreviewed | Yes | en |
local.identifier.volume | 30 | en |
local.identifier.issue | 8 | en |
local.contributor.lastname | Guo | en |
local.contributor.lastname | Gao | en |
local.contributor.lastname | Kwan | en |
dc.identifier.staff | une-id:yguo4 | en |
dc.identifier.staff | une-id:jgao | 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:3580 | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
dc.identifier.academiclevel | Academic | en |
local.title.maintitle | Twin Kernel Embedding | en |
local.output.categorydescription | C1 Refereed Article in a Scholarly Journal | en |
local.search.author | Guo, Yi | en |
local.search.author | Gao, Junbin | en |
local.search.author | Kwan, Paul Hing | en |
local.uneassociation | Unknown | en |
local.year.published | 2008 | en |
Appears in Collections: | Journal Article |
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