Subspace, Latent Structure and Feature Selection

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

Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor (Herausgeber)

Buch | Softcover

X, 209 Seiten

2006 | 2006
Springer Berlin (Verlag)
978-3-540-34137-6 (ISBN)

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lt;p>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.

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.

Erscheint lt. Verlag	16.5.2006
Reihe/Serie	Lecture Notes in Computer Science
Reihe/Serie	Theoretical Computer Science and General Issues
Zusatzinfo	X, 209 p.
Verlagsort	Berlin
Sprache	englisch
Maße	155 x 235 mm
Gewicht	322 g
Themenwelt	Informatik ► Theorie / Studium ► Algorithmen
Themenwelt	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
Schlagworte	3D • algorithm • Algorithm analysis and problem complexity • Algorithmic Learning • algorithms • Bayesian inference • Calculus • Clustering • dimension reduction • Feature Selection • image reconstruction • latent structure analysis • learning • machine learning • optimisation methods • Optimization • Statistica • Statistical Analysis • Statistical Learning • statistical modeling
ISBN-10	3-540-34137-4 / 3540341374
ISBN-13	978-3-540-34137-6 / 9783540341376
Zustand	Neuware