Spectral Feature Selection for Data Mining
Proposal review
| dc.contributor.author | Zhao, Zheng Alan | |
| dc.contributor.author | Liu, Huan | |
| dc.date.accessioned | 2025-04-22T11:38:46Z | |
| dc.date.available | 2025-04-22T11:38:46Z | |
| dc.date.issued | 2011 | |
| dc.identifier | ONIX_20250422_9781439862100_21 | |
| dc.identifier | ONIX_20250422_9781439862100_21a | |
| dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/101027 | |
| dc.description.abstract | Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise | |
| dc.language | English | |
| dc.relation.ispartofseries | Chapman & Hall/CRC Data Mining and Knowledge Discovery Series | |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UY Computer science | |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining | |
| dc.subject.classification | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering | |
| dc.subject.classification | thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics | |
| dc.subject.other | Feature Selection Algorithms | |
| dc.subject.other | Feature Selection | |
| dc.subject.other | Spectral Feature Selection | |
| dc.subject.other | Multivariate Formulations | |
| dc.subject.other | data mining | |
| dc.subject.other | Data Set | |
| dc.subject.other | machine learning | |
| dc.subject.other | Fisher Score | |
| dc.subject.other | dimensionality reduction | |
| dc.subject.other | Laplacian Matrix | |
| dc.subject.other | Similarity Matrix | |
| dc.subject.other | feature extraction | |
| dc.subject.other | high-dimensional data processing | |
| dc.subject.other | Redundant Features | |
| dc.subject.other | Existing Feature Selection | |
| dc.subject.other | Normalized Laplacian Matrix | |
| dc.subject.other | Rank Aggregation | |
| dc.subject.other | F2 F3 F4 F5 F6 | |
| dc.subject.other | Feature Selection Techniques | |
| dc.subject.other | F1 F2 F3 F4 F5 | |
| dc.subject.other | TIMP Metallopeptidase Inhibitor | |
| dc.subject.other | Gene Selection | |
| dc.subject.other | Computer Nodes | |
| dc.subject.other | microRNA Microarray | |
| dc.subject.other | LDA | |
| dc.title | Spectral Feature Selection for Data Mining | |
| dc.type | book | |
| oapen.identifier.doi | 10.1201/b11426 | |
| oapen.relation.isPublishedBy | 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb | |
| oapen.relation.isFundedBy | b818ba9d-2dd9-4fd7-a364-7f305aef7ee9 | |
| oapen.relation.isbn | 9781439862100 | |
| oapen.relation.isbn | 9781138112629 | |
| oapen.relation.isbn | 9781439862094 | |
| oapen.relation.isbn | 9781000023046 | |
| oapen.relation.isbn | 9780429107191 | |
| oapen.relation.isbn | 9781000023077 | |
| oapen.collection | Knowledge Unlatched (KU) | |
| oapen.imprint | Chapman and Hall/CRC | |
| oapen.pages | 224 | |
| oapen.grant.number | [...] | |
| oapen.identifier.ocn | 773311146 | |
| peerreview.anonymity | Single-anonymised | |
| peerreview.id | bc80075c-96cc-4740-a9f3-a234bc2598f1 | |
| peerreview.open.review | No | |
| peerreview.publish.responsibility | Publisher | |
| peerreview.review.stage | Pre-publication | |
| peerreview.review.type | Proposal | |
| peerreview.reviewer.type | Internal editor | |
| peerreview.reviewer.type | External peer reviewer | |
| peerreview.title | Proposal review | |
| oapen.review.comments | Taylor & Francis open access titles are reviewed as a minimum at proposal stage by at least two external peer reviewers and an internal editor (additional reviews may be sought and additional content reviewed as required). |
