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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Saunders, Craig (Επιμελητής έκδοσης), Grobelnik, Marko (Επιμελητής έκδοσης), Gunn, Steve (Επιμελητής έκδοσης), Shawe-Taylor, John (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
• Σειρά: Lecture Notes in Computer Science, 3940
• Θέματα: Computer science.; Computers.; Algorithms.; Mathematical statistics.; Artificial intelligence.; Image processing.; Pattern recognition.; Computer Science.; Algorithm Analysis and Problem Complexity.; Probability and Statistics in Computer Science.; Computation by Abstract Devices.; Artificial Intelligence (incl. Robotics).; Image Processing and Computer Vision.; Pattern Recognition.

 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.