Subspace Latent Structure and Feature Selection : Statistical and Optimization Perspectives Workshop SLSFS 2005Bohinj Slovenia February 23 25 2005 Revised Selected Papers 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.