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

Name: Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 2005, Revised Selected Papers
Price: 35.75 USD
Availability: InStock
ISBN: 9783540341376

ISBN-10: 3540341374

ISBN-13: 9783540341376

Edition: 2006

Authors: Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor

List price: $54.99

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Description:

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, held in Bohinj, Slovenia in February 2005.The 9 revised full papers presented together with 5 invited papers were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, statistical analysis…

Book details

List price: $54.99
Copyright year: 2006
Publisher: Springer Berlin / Heidelberg
Publication date: 5/16/2006
Binding: Paperback
Pages: 209
Size: 6.10" wide x 9.25" long x 0.50" tall
Weight: 1.562



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



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