Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Saunders, Craig (Editor ), Grobelnik, Marko (Editor ), Gunn, Steve (Editor ), Shawe-Taylor, John (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edición:1st ed. 2006.
Colección:Theoretical Computer Science and General Issues, 3940
Temas:
Algorithms.
Computer science-Mathematics.
Mathematical statistics.
Computer science.
Artificial intelligence.
Computer vision.
Pattern recognition systems.
Probability and Statistics in Computer Science.
Theory of Computation.
Artificial Intelligence.
Computer Vision.
Automated Pattern Recognition.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Invited Contributions
  • Discrete Component Analysis
  • Overview and Recent Advances in Partial Least Squares
  • Random Projection, Margins, Kernels, and Feature-Selection
  • Some Aspects of Latent Structure Analysis
  • Feature Selection for Dimensionality Reduction
  • Contributed Papers
  • Auxiliary Variational Information Maximization for Dimensionality Reduction
  • Constructing Visual Models with a Latent Space Approach
  • Is Feature Selection Still Necessary?
  • Class-Specific Subspace Discriminant Analysis for High-Dimensional Data
  • Incorporating Constraints and Prior Knowledge into Factorization Algorithms - An Application to 3D Recovery
  • A Simple Feature Extraction for High Dimensional Image Representations
  • Identifying Feature Relevance Using a Random Forest
  • Generalization Bounds for Subspace Selection and Hyperbolic PCA
  • Less Biased Measurement of Feature Selection Benefits.

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