Subspace, Latent Structure and Feature Selection [electronic resource] : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers /
SLSFS 2005Corporate Author
SLSFS 2005(2005 :Bohinj, Slovenia)
Saunders, Craig.editor.
Grobelnik, Marko.editor.
Gunn, Steve.editor.
Shawe-Taylor, John.editor.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and author index.
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.
Algorithms.
Mathematical statistics.
Computers.
Artificial intelligence.
Optical data processing.
Pattern recognition.
Algorithm Analysis and Problem Complexity.
Probability and Statistics in Computer Science.
Computation by Abstract Devices.
Artificial Intelligence.
Image Processing and Computer Vision.
Pattern Recognition.
Lecture notes in computer science