Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers /
Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / [electronic resource] :
edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor.
- 1st ed. 2006.
- X, 209 p. online resource.
- Theoretical Computer Science and General Issues, 3940 2512-2029 ; .
- Theoretical Computer Science and General Issues, 3940 .
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.
9783540341383
10.1007/11752790 doi
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.
QA76.9.A43
518.1
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.
9783540341383
10.1007/11752790 doi
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.
QA76.9.A43
518.1