Multi-label dimensionality reduction / / Liang Sun, Shuiwang Ji, and Jieping Ye

• Autore: Sun Liang
• Pubblicazione: Boca Raton, FL : , : CRC Press, , [2014]; ©2014
• Edizione: 1st edition
• Descrizione fisica: 1 online resource (206 p.)
• Disciplina: 006.3/1
• Soggetto topico: Computational complexity; Machine learning; Pattern perception
• Soggetto genere / forma: Electronic books.
• Persona (resp. second.): JiShuiwang; YeJieping
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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
• 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
• Titolo autorizzato: Multi-label dimensionality reduction
• ISBN: 0-429-14820-8; 1-4398-0616-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910453732903321
• Lo trovi qui: Univ. Federico II
• Serie: Chapman & Hall/CRC machine learning & pattern recognition series.