Spectral Feature Selection for Data Mining

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 framewo...

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Κύριοι συγγραφείς:	Zhao, Zheng Alan, Liu, Huan
Μορφή:	Online
Γλώσσα:	Αγγλικά
Έκδοση:	Taylor & Francis 2025
Θέματα:	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 thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
Διαθέσιμο Online:	ONIX_20250422_9781439862100_21
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Περίληψη:	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

Spectral Feature Selection for Data Mining

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