Workshop on Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives, Bohinj 2005

Workshop on Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives, Bohinj 2005

20 Lectures · Feb 22, 2005

About

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.

Related categories

Uploaded videos:

Lectures

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58:39

Latent Semantic Variable Models

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Some aspects of Latent Structure Analysis

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40:19

Discrete PCA

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25:35

In search of Non-Gaussian Components of a High-Dimensional Distribution

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Auxillary Variational Information Maximization for Dimensionality Reduction

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Dimensionality Reduction in Gaussian Process Models

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Modelling Intra-Speaker Variability for Improved Speaker Recognition

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53:22

Dimensionality Reduction by Feature Selection in Machine Learning

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Semantic text features from small world graphs

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Sparsity analsysis of term weighting schemes and application to text classificat...

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Greedy Feature Grouping for Optimal Discriminant Subspaces

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Constructing visual models with a latent space approach

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What is the Optimal Number of Features? A learning theoretic perspective

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A statistical learning approach to subspace identification of dynamical systems

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Classification of high dimensional data: High Dimensional Discriminant Analysis

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21:44

Random projection, margins, kernels, and feature-selection

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A simple feature extraction for high dimensional image representations

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Feature-Learning from Pairs of Examples in Collections of Supervised Learning Ta...

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Identifying Feature Relevance using a Random Forest

Jeremy D. Rogers

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Online feature selection for contextual time series data

Petteri Nurmi

Feb 25, 2007

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