dc.contributor.author | Wolf, Guy | |
dc.date.accessioned | 2013-12-09T11:01:48Z | |
dc.date.available | 2013-12-09T11:01:48Z | |
dc.date.issued | 2013 | |
dc.identifier.isbn | 978-951-39-5534-2 | |
dc.identifier.other | oai:jykdok.linneanet.fi:1288688 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/42611 | |
dc.format.extent | 1 verkkoaineisto (29 sivua) | |
dc.language.iso | eng | |
dc.publisher | University of Jyväskylä | |
dc.relation.ispartofseries | Jyväskylä studies in computing | |
dc.relation.haspart | <b>Article I:</b> Guy Wolf, Aviv Rotbart, Gil David, and Amir Averbuch. Coarse-grained localized diffusion.<i> Applied and Computational Harmonic Analysis, 33(3):388– 400, 2012.</i><a href="http://dx.doi.org/10.1016/j.acha.2012.02.004"> DOI: 10.1016/j.acha.2012.02.004</a> | |
dc.relation.haspart | <b>Article II:</b> Moshe Salhov, Guy Wolf, Amir Averbuch. Patch-to-Tensor Embedding. <i>Applied and Computational Harmonic Analysis, 33(2):182–203, 2012.</i> <a href="http://dx.doi.org/10.1016/j.acha.2011.11.003"> DOI: 10.1016/j.acha.2011.11.003</a> | |
dc.relation.haspart | <b>Article III:</b> Guy Wolf and Amir Averbuch. Linear-Projection Diffusion on Smooth Euclidean Submanifolds. <i>Applied and Computational Harmonic Analysis, 34(1):1–14, 2013. </i> <a href="http://dx.doi.org/10.1016/j.acha.2012.03.003"> DOI: 10.1016/j.acha.2012.03.003</a> | |
dc.relation.haspart | <b>Article IV:</b> Moshe Salhov, Amit Bermanis, Guy Wolf, Amir Averbuch. Approximate Patch-to-Tensor Embedding via Dictionary Construction. <i>Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. </i> | |
dc.relation.haspart | <b>Article V:</b> Yaniv Shmuelli, Guy Wolf, Amir Averbuch. Updating kernel methods in spectral decomposition by affinityperturbations. <i>Linear Algebra and its Applications, 437(6):1356–1365, 2012. </i><a href="http://dx.doi.org/10.1016/j.laa.2012.04.035"> DOI: 10.1016/j.laa.2012.04.035</a> | |
dc.subject.other | big data | |
dc.subject.other | data analysis | |
dc.subject.other | manifold learning | |
dc.subject.other | diffusion maps | |
dc.title | Big high-dimensional data analysis with diffusion maps | |
dc.type | Diss. | |
dc.identifier.urn | URN:ISBN:978-951-39-5534-2 | |
dc.type.dcmitype | Text | en |
dc.type.ontasot | Väitöskirja | fi |
dc.type.ontasot | Doctoral dissertation | en |
dc.contributor.tiedekunta | Informaatioteknologian tiedekunta | fi |
dc.contributor.tiedekunta | Faculty of Information Technology | en |
dc.contributor.yliopisto | University of Jyväskylä | en |
dc.contributor.yliopisto | Jyväskylän yliopisto | fi |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.relation.issn | 1456-5390 | |
dc.relation.numberinseries | 183 | |
dc.rights.accesslevel | openAccess | fi |
dc.subject.yso | data | |
dc.subject.yso | big data | |
dc.subject.yso | analyysimenetelmät | |
dc.subject.yso | algoritmit | |
dc.subject.yso | koneoppiminen | |