Format:
Online-Ressource (X, 209 p. Also available online, digital)
ISBN:
9783540341383
Series Statement:
Lecture Notes in Computer Science 3940
Content:
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.
Note:
Lizenzpflichtig
Additional Edition:
ISBN 9783540341376
Additional Edition:
Buchausg. u.d.T. Subspace, latent structure and feature selection Berlin : Springer, 2006 ISBN 3540341374
Additional Edition:
ISBN 9783540341376
Language:
English
Subjects:
Computer Science
Keywords:
Unterraumsuche
;
Analyse latenter Strukturen
;
Merkmalsextraktion
;
Unterraumsuche
;
Analyse latenter Strukturen
;
Merkmalsextraktion
;
Konferenzschrift
URL:
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(lizenzpflichtig)
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