Subspace, Latent Structure and Feature Selection (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
209
Utgivningsdatum
2006-05-01
Upplaga
2006 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Saunders, Craig (ed.), Grobelnik, Marko (ed.), Gunn, Steve (ed.), Shawe-Taylor, John (ed.)
Illustrationer
X, 209 p.
Dimensioner
234 x 156 x 12 mm
Vikt
318 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540341376
Subspace, Latent Structure and Feature Selection (häftad)

Subspace, Latent Structure and Feature Selection

Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers

Häftad Engelska, 2006-05-01
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This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers presented together with 5 invited papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, among others.
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Innehållsförteckning

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.