Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / [electronic resource] : edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor. - 1st ed. 2006. - X, 209 p. online resource. - Theoretical Computer Science and General Issues, 3940 2512-2029 ; . - Theoretical Computer Science and General Issues, 3940 .

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.

9783540341383

10.1007/11752790 doi


Algorithms.
Computer science--Mathematics.
Mathematical statistics.
Computer science.
Artificial intelligence.
Computer vision.
Pattern recognition systems.
Algorithms.
Probability and Statistics in Computer Science.
Theory of Computation.
Artificial Intelligence.
Computer Vision.
Automated Pattern Recognition.

QA76.9.A43

518.1