Generalized principal component analysis / René Vidal, Yi Ma, S. Shankar Sastry

• Autore: Vidal, René
• Pubblicazione: New York, : Springer, 2016
• Titolo uniforme: Generalized principal component analysis
• Descrizione fisica: XXXII, 566 p. : ill. ; 24 cm
• Soggetto topico: 14-XX - Algebraic geometry [MSC 2020]; 15-XX - Linear and multilinear algebra; matrix theory [MSC 2020]; 62B10 - Statistical aspects of information-theoretic topics [MSC 2020]; 62H12 - Estimation in multivariate analysis [MSC 2020]; 62-XX - Statistics [MSC 2020]; 62J05 - Linear regression; mixed models [MSC 2020]; 30C10 - Polynomials and rational functions of one complex variable [MSC 2020]; 62H35 - Image analysis in multivariate analysis [MSC 2020]; 62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020]; 62Jxx - Linear inference, regression [MSC 2020]; 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020]; 62Fxx - Parametric inference [MSC 2020]; 30C40 - Kernel functions in one complex variable and applications [MSC 2020]; 14N20 - Configurations and arrangements of linear subspaces [MSC 2020]
• Soggetto non controllato: Hybrid system identification; Image and video segmentation; Linear subspace models; Low-rank matrix theory; Manifold learning; Principal component analysis; Robust principal component analysis; Sparse representation theory; Spectral clustering; Subspace arrangements; Subspace clustering
• Altri autori: Ma, Yi Sastry, S. Shankar
• Titolo autorizzato: Generalized principal component analysis
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN0114796
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: http://dx.doi.org/10.1007/978-0-387-87811-9
• Serie: Interdisciplinary applied mathematics New York [etc.] . -Springer ; 40