Publication
Exploiting low-rank approximations of kernel matrics in denoising applicationS
| dc.contributor.author | Teixeira, Ana | |
| dc.contributor.author | Tomé, A. M. | |
| dc.contributor.author | Lang, E.W. | |
| dc.date.accessioned | 2023-10-20T14:16:50Z | |
| dc.date.available | 2023-10-20T14:16:50Z | |
| dc.date.issued | 2007 | |
| dc.description.abstract | The eigendecomposition of a kernel matrix can present a computational burden in many kernel methods. Nevertheless only the largest eigenvalues and corresponding eigenvectors need to be computed. In this work we discuss the Nystrom low-rank approximations of the kernel matrix and its applications in KPCA denoising tasks. Furthermore, the low-rank approximations have the advantage of being related with a smaller subset of the training data which constitute then a basis of a subspace. In a common algebraic framework we discuss the different approaches to compute the basis. Numerical simulations concerning the denoising are presented to compare the discussed approaches. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.26/47374 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.title | Exploiting low-rank approximations of kernel matrics in denoising applicationS | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | [Thessaloniki] | pt_PT |
| oaire.citation.endPage | 347 | pt_PT |
| oaire.citation.startPage | 342 | pt_PT |
| oaire.citation.title | [IEEE Workshop on Machine Learning for Signal Processing] | pt_PT |
| person.familyName | Teixeira | |
| person.givenName | Ana | |
| person.identifier.ciencia-id | D619-A151-8BE2 | |
| person.identifier.orcid | 0000-0002-8120-0148 | |
| person.identifier.rid | A-3100-2014 | |
| person.identifier.scopus-author-id | 7202385348 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | c1ff686d-c3d3-4658-96c6-a1f62a52777a | |
| relation.isAuthorOfPublication.latestForDiscovery | c1ff686d-c3d3-4658-96c6-a1f62a52777a |
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