Subspace, Latent Structure and Feature Selection [electronic resource] : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers /
Material type: TextSeries: Theoretical Computer Science and General Issues ; 3940Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006Edition: 1st ed. 2006Description: X, 209 p. online resourceContent type:- text
- computer
- online resource
- 9783540341383
- Algorithms
- Computer science -- Mathematics
- Mathematical statistics
- Computer science
- Artificial intelligence
- Computer vision
- Pattern recognition systems
- Algorithms
- Probability and Statistics in Computer Science
- Theory of Computation
- Artificial Intelligence
- Computer Vision
- Automated Pattern Recognition
- 518.1 23
- QA76.9.A43
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.
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