Subspace, Latent Structure and Feature Selection : Statistical and Optimization Perspectives Workshop, Slsfs 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers - Craig Saunders

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Subspace, Latent Structure and Feature Selection

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

By: Craig Saunders (Editor), Marko Grobelnik (Editor), Steve Gunn (Editor)

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Paperback | 16 May 2006

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224 Pages

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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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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Programming & Scripting Languages Mathematics Probability & Statistics Business Applications Mathematical & Statistical Software Algorithms & Data Structures Graphical & Digital Media Applications Computer Science Artificial Intelligence

Computer Vision Pattern Recognition

Discrete component analysis	p. 1
Overview and recent advances in partial least squares	p. 34
Random projection, margins, kernels, and feature-selection	p. 52
Some aspects of latent structure analysis	p. 69
Feature selection for dimensionality reduction	p. 84
Auxiliary variational information maximization for dimensionality reduction	p. 103
Constructing visual models with a latent space approach	p. 115
Is feature selection still necessary?	p. 127
Class-specific subspace discriminant analysis for high-dimensional data	p. 139
Incorporating constraints and prior knowledge into factorization algorithms - an application to 3D recovery	p. 151
A simple feature extraction for high dimensional image representations	p. 163
Identifying feature relevance using a random forest	p. 173
Generalization bounds for subspace selection and hyperbolic PCA	p. 185
Less biased measurement of feature selection benefits	p. 198
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Subspace, Latent Structure and Feature Selection

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

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