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[null,"1.","Record Nr.",null,"UNINA9910453732903321"]
[null,null,"Autore",null,"Sun Liang"]
[null,null,"Titolo",null,"Multi-label dimensionality reduction / / Liang Sun, Shuiwang Ji, and Jieping Ye"]
[null,null,"Pubbl/distr/stampa",null,"Boca Raton, FL : , : CRC Press, , [2014]"]
[{"$nulls":4},"©2014"]
[null,null,"ISBN",null,"0-429-14820-8"]
[{"$nulls":4},"1-4398-0616-0"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (206 p.)"]
[null,null,"Collana",null,"Chapman & Hall/CRC machine learning & pattern recognition series"]
[null,null,"Disciplina",null,"006.3/1"]
[null,null,"Soggetti",null,"Computational complexity"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Pattern perception"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Cover; Series; Contents; Preface; Symbol Description; Chapter 1: Introduction; Chapter 2: Partial Least Squares; Chapter 3: Canonical Correlation Analysis; Chapter 4: Hypergraph Spectral Learning; Chapter 5: A Scalable Two-Stage Approach for Dimensionality Reduction; Chapter 6: A Shared-Subspace Learning Framework; Chapter 7: Joint Dimensionality Reduction and Classification; Chapter 8: Nonlinear Dimensionality Reduction: Algorithms and Applications; Appendix Proofs; References; Back Cover"]
[null,null,null,"Sommario/riassunto","Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications. Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properti"]