Cross-view kernel transfer
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.author | Huusari, Riikka | |
| dc.contributor.author | Capponi, Cécile | |
| dc.contributor.author | Villoutreix, Paul | |
| dc.contributor.author | Kadri, Hachem | |
| dc.contributor.department | Department of Computer Science | |
| dc.contributor.department | Aix-Marseille Université | |
| dc.date.accessioned | 2022-05-24T05:13:34Z | |
| dc.date.available | 2022-05-24T05:13:34Z | |
| dc.date.issued | 2022-09 | |
| dc.description | Funding Information: This work is mainly granted by the french national project ANR Lives ANR-15-CE23-0026, and by the Turing Center for Living Systems (CENTURI) for PV. For the most part work by RH has been done in Aix-Marseille University – the part in Aalto University has been funded by Academy of Finland grants 334790 (MAGITICS) and 310107 (MACOME). Publisher Copyright: © 2022 The Author(s) | |
| dc.description.abstract | We consider the kernel completion problem with the presence of multiple views in the data. In this context the data samples can be fully missing in some views, creating missing columns and rows to the kernel matrices that are calculated individually for each view. We propose to solve the problem of completing the kernel matrices with Cross-View Kernel Transfer (CVKT) procedure, in which the features of the other views are transformed to represent the view under consideration. The transformations are learned with kernel alignment to the known part of the kernel matrix, allowing for finding generalizable structures in the kernel matrix under completion. Its missing values can then be predicted with the data available in other views. We illustrate the benefits of our approach with simulated data, multivariate digits dataset and multi-view dataset on gesture classification, as well as with real biological datasets from studies of pattern formation in early Drosophila melanogaster embryogenesis. | en |
| dc.description.version | Peer reviewed | en |
| dc.format.extent | 14 | |
| dc.format.extent | 1-14 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Huusari , R , Capponi , C , Villoutreix , P & Kadri , H 2022 , ' Cross-view kernel transfer ' , Pattern Recognition , vol. 129 , 108759 , pp. 1-14 . https://doi.org/10.1016/j.patcog.2022.108759 | en |
| dc.identifier.doi | 10.1016/j.patcog.2022.108759 | |
| dc.identifier.issn | 0031-3203 | |
| dc.identifier.other | PURE UUID: c460f993-0a0b-4a04-8c3d-22683dda26cf | |
| dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/c460f993-0a0b-4a04-8c3d-22683dda26cf | |
| dc.identifier.other | PURE LINK: http://www.scopus.com/inward/record.url?scp=85129746995&partnerID=8YFLogxK | |
| dc.identifier.other | PURE LINK: https://www.sciencedirect.com/science/article/pii/S0031320322002400 | |
| dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/83364575/Cross_View_kernel_transfer.pdf | |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/114559 | |
| dc.identifier.urn | URN:NBN:fi:aalto-202205243406 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier Limited | |
| dc.relation.ispartofseries | Pattern Recognition | en |
| dc.relation.ispartofseries | Volume 129 | en |
| dc.rights | openAccess | en |
| dc.subject.keyword | Cross-view transfer | |
| dc.subject.keyword | Kernel completion | |
| dc.subject.keyword | Kernel learning | |
| dc.subject.keyword | Multi-view learning | |
| dc.title | Cross-view kernel transfer | en |
| dc.type | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä | fi |
| dc.type.version | publishedVersion |
