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Dimensionality Reduction in Gaussian Process Models

Dimensionality Reduction in Gaussian Process Models

This video was recorded at Workshop on Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives, Bohinj 2005. The workshop examines and invites discussion on a range of methods that have been developed for dimension reduction and feature selection. This is a core topic which has been addressed theoretically in many guises from the perspectives of boosting, eigenanalysis, optimisation, latent structure analysis, bayesian methods and traditional statistical approaches to name a few. As an applied technique many algorithms exist for feature selection and all real-world applications of machine learning include some aspect of this in their implementation. In line with the Thematic Programme 'Linking Learning and Statistics with Optimisation' the workshop focuses on the integration between for example the statistical (frequentist and Bayesian) aspects as well as optimisation issues raised by subspace identification. We feel the workshop provides a real opportunity for interaction between different areas of research and its focus on a strongly applicable family of methods will promote active discussion between different areas of the research community. Topics considered and contributions are sought in the following areas: Dimension reduction techniques, subspace methods Random projection methods Boosting Statistical analysis methods Bayesian approaches to feature selection Latent structure analysis/Probabilistic LSA Optimisation methods Novel applications of feature selection algorithms Open problems in the domain More information can be found here.


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