Spectral Feature Selection for Data Mining

Proposal review

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Author(s)
Zhao, Zheng Alan
Liu, Huan
Collection
Knowledge Unlatched (KU)
Language
English
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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
URI
https://library.oapen.org/handle/20.500.12657/101027
Keywords
Feature Selection Algorithms; Feature Selection; Spectral Feature Selection; Multivariate Formulations; data mining; Data Set; machine learning; Fisher Score; dimensionality reduction; Laplacian Matrix; Similarity Matrix; feature extraction; high-dimensional data processing; Redundant Features; Existing Feature Selection; Normalized Laplacian Matrix; Rank Aggregation; F2 F3 F4 F5 F6; Feature Selection Techniques; F1 F2 F3 F4 F5; TIMP Metallopeptidase Inhibitor; Gene Selection; Computer Nodes; microRNA Microarray; LDA
DOI
10.1201/b11426
ISBN
9781439862100, 9781138112629, 9781439862094, 9781000023046, 9780429107191, 9781000023077, 9781439862100
OCN
773311146
Publisher
Taylor & Francis
Publisher website
https://taylorandfrancis.com/
Publication date and place
2011
Grantor
Imprint
Chapman and Hall/CRC
Series
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series,
Classification
Computer science
Data mining
Automatic control engineering
Probability and statistics
Pages
224
Rights
https://creativecommons.org/licenses/by-nc-nd/4.0/