Sparse modeling : theory, algorithms, and applications /

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algo...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Rish, Irina, 1969- (Autor), Grabarnik, Genady Ya (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press, [2015]
Colección:Chapman & Hall/CRC machine learning & pattern recognition series.
Temas:
Mathematical models.
Sampling (Statistics)
Data reduction.
Sparse matrices.
Modèles mathématiques.
Échantillonnage (Statistique)
Réduction des données (Statistique)
Matrices éparses.
mathematical models.
SCIENCE > System Theory.
TECHNOLOGY & ENGINEERING > Operations Research.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 1. Introduction
  • 2. Sparse recovery : problem formulations
  • 3. Theoretical results (deterministic part)
  • 4. Theoretical results (probabilistic part)
  • 5. Algorithms for sparse recovery problems
  • 6. Beyond LASSO : structured sparsity
  • 7. Beyond LASSO : other loss functions
  • 8. Sparse graphical models
  • 9. Sparse matrix factorization : dictionary learning and beyond.

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