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 .
<br/><br/>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. 
<br/><br/><label>ISBN: </label>9783540341383 
<br/><br/><label>Standard No.: </label>10.1007/11752790  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Algorithms.<br/>Computer science--Mathematics.<br/>Mathematical statistics.<br/>Computer science.<br/>Artificial intelligence.<br/>Computer vision.<br/>Pattern recognition systems.<br/>Algorithms.<br/>Probability and Statistics in Computer Science.<br/>Theory of Computation.<br/>Artificial Intelligence.<br/>Computer Vision.<br/>Automated Pattern Recognition.
<br/><br/><label>LC Class. No.: </label>QA76.9.A43 
<br/><br/><label>Dewey Class. No.: </label>518.1