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- Publisher Website: 10.1109/MSP.2010.940005
- Scopus: eid_2-s2.0-85032750821
- WOS: WOS:000287662000004
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Article: Dimensionality reduction via subspace and submanifold learning
Title | Dimensionality reduction via subspace and submanifold learning |
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Authors | |
Keywords | Audio databases Information analysis Learning systems Search problems Special issues and sections Web services |
Issue Date | 2011 |
Citation | IEEE Signal Processing Magazine, 2011, v. 28, n. 2, article no. 5714387 How to Cite? |
Abstract | The problem of finding and exploiting low-dimensional structures in high-dimensional data is taking on increasing importance in image, video, or audio processing; Web data analysis/search; and bioinformatics, where data sets now routinely lie in observational spaces of thousands, millions, or even billions of dimensions. The curse of dimensionality is in full play here: We often need to conduct meaningful inference with a limited number of samples in a very high-dimensional space. Conventional statistical and computational tools have become severely inadequate for processing and analyzing such high-dimensional data. © 2006 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/327158 |
ISSN | 2023 Impact Factor: 9.4 2023 SCImago Journal Rankings: 4.896 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ma, Yi | - |
dc.contributor.author | Niyogi, Partha | - |
dc.contributor.author | Sapiro, Guillermo | - |
dc.contributor.author | Vidal, Rene | - |
dc.date.accessioned | 2023-03-31T05:29:23Z | - |
dc.date.available | 2023-03-31T05:29:23Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | IEEE Signal Processing Magazine, 2011, v. 28, n. 2, article no. 5714387 | - |
dc.identifier.issn | 1053-5888 | - |
dc.identifier.uri | http://hdl.handle.net/10722/327158 | - |
dc.description.abstract | The problem of finding and exploiting low-dimensional structures in high-dimensional data is taking on increasing importance in image, video, or audio processing; Web data analysis/search; and bioinformatics, where data sets now routinely lie in observational spaces of thousands, millions, or even billions of dimensions. The curse of dimensionality is in full play here: We often need to conduct meaningful inference with a limited number of samples in a very high-dimensional space. Conventional statistical and computational tools have become severely inadequate for processing and analyzing such high-dimensional data. © 2006 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Signal Processing Magazine | - |
dc.subject | Audio databases | - |
dc.subject | Information analysis | - |
dc.subject | Learning systems | - |
dc.subject | Search problems | - |
dc.subject | Special issues and sections | - |
dc.subject | Web services | - |
dc.title | Dimensionality reduction via subspace and submanifold learning | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/MSP.2010.940005 | - |
dc.identifier.scopus | eid_2-s2.0-85032750821 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | article no. 5714387 | - |
dc.identifier.epage | article no. 5714387 | - |
dc.identifier.isi | WOS:000287662000004 | - |