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ORCIDSLSFS 2005: Bohinj, Slovenia
- Craig Saunders, Marko Grobelnik, Steve R. Gunn, John Shawe-Taylor:
Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers. Lecture Notes in Computer Science 3940, Springer 2006, ISBN 3-540-34137-4
Invited Contributions
- Wray L. Buntine
, Aleks Jakulin:
Discrete Component Analysis. 1-33 - Roman Rosipal, Nicole Krämer:
Overview and Recent Advances in Partial Least Squares. 34-51 - Avrim Blum:
Random Projection, Margins, Kernels, and Feature-Selection. 52-68 - D. Mike Titterington:
Some Aspects of Latent Structure Analysis. 69-83 - Dunja Mladenic:
Feature Selection for Dimensionality Reduction. 84-102
Contributed Papers
- Felix V. Agakov, David Barber:
Auxiliary Variational Information Maximization for Dimensionality Reduction. 103-114 - Florent Monay, Pedro Quelhas, Daniel Gatica-Perez, Jean-Marc Odobez:
Constructing Visual Models with a Latent Space Approach. 115-126 - Amir Navot, Ran Gilad-Bachrach, Yiftah Navot, Naftali Tishby:
Is Feature Selection Still Necessary? 127-138 - Charles Bouveyron, Stéphane Girard, Cordelia Schmid:
Class-Specific Subspace Discriminant Analysis for High-Dimensional Data. 139-150 - Amit Gruber, Yair Weiss:
Incorporating Constraints and Prior Knowledge into Factorization Algorithms - An Application to 3D Recovery. 151-162 - Christian Savu-Krohn, Peter Auer:
A Simple Feature Extraction for High Dimensional Image Representations. 163-172 - Jeremy Rogers, Steve R. Gunn:
Identifying Feature Relevance Using a Random Forest. 173-184 - Andreas Maurer:
Generalization Bounds for Subspace Selection and Hyperbolic PCA. 185-197 - Juha Reunanen:
Less Biased Measurement of Feature Selection Benefits. 198-208
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