Spectral Feature Selection for Data Mining
dc.contributor.author | Zhao, Zheng Alan | |
dc.contributor.author | Liu, Huan | |
dc.date.accessioned | 2021-02-10T12:58:18Z | |
dc.date.issued | 2012-01-01 | |
dc.date.submitted | 2019-04-11 23:55 | |
dc.date.submitted | 2020-03-17 03:00:34 | |
dc.date.submitted | 2020-04-01T10:32:49Z | |
dc.identifier | 1004820 | |
dc.identifier | OCN: 773311146 | |
dc.identifier | http://library.oapen.org/handle/20.500.12657/25274 | |
dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/36827 | |
dc.description.abstract | This timely introduction to spectral feature selection illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. It presents the theoretical foundations of spectral feature selection, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection. Source code for the algorithms is available online. | |
dc.language | English | |
dc.rights | open access | |
dc.subject.classification | bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining | |
dc.subject.other | Computer Science | |
dc.title | Spectral Feature Selection for Data Mining | |
dc.type | book | |
oapen.identifier.doi | 10.1201/b11426 | |
oapen.relation.isPublishedBy | fa69b019-f4ee-4979-8d42-c6b6c476b5f0 | |
oapen.relation.isFundedBy | 969f21b5-ac00-4517-9de2-44973eec6874 | |
oapen.relation.isFundedBy | b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 | |
oapen.collection | Knowledge Unlatched (KU) | |
oapen.grant.number | 102700 | |
oapen.grant.program | KU Select 2018: STEM Backlist Books | |
dc.number | 102700 | |
dc.relationisFundedBy | b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 |
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