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.
Videos
Lectures

Some aspects of Latent Structure Analysis
Feb 25, 2007
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8133 views

Sparsity analsysis of term weighting schemes and application to text classificat...
Feb 25, 2007
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3445 views

Auxillary Variational Information Maximization for Dimensionality Reduction
Feb 25, 2007
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4643 views

Online feature selection for contextual time series data
Feb 25, 2007
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3531 views

Random projection, margins, kernels, and feature-selection
Feb 25, 2007
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7694 views

Dimensionality Reduction by Feature Selection in Machine Learning
Feb 25, 2007
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17291 views

A simple feature extraction for high dimensional image representations
Feb 25, 2007
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5250 views

Constructing visual models with a latent space approach
Feb 25, 2007
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3084 views

A statistical learning approach to subspace identification of dynamical systems
Feb 25, 2007
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6743 views

Classification of high dimensional data: High Dimensional Discriminant Analysis
Feb 25, 2007
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4701 views

Semantic text features from small world graphs
Feb 25, 2007
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6559 views

Identifying Feature Relevance using a Random Forest
Feb 25, 2007
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12620 views

What is the Optimal Number of Features? A learning theoretic perspective
Feb 25, 2007
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6908 views

Feature-Learning from Pairs of Examples in Collections of Supervised Learning Ta...
Feb 25, 2007
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3445 views

Discrete PCA
Feb 25, 2007
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7197 views

Latent Semantic Variable Models
Feb 25, 2007
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29761 views

Modelling Intra-Speaker Variability for Improved Speaker Recognition
Feb 25, 2007
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4783 views

Dimensionality Reduction in Gaussian Process Models
Feb 25, 2007
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4980 views

In search of Non-Gaussian Components of a High-Dimensional Distribution
Feb 25, 2007
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4505 views

Greedy Feature Grouping for Optimal Discriminant Subspaces
Feb 25, 2007
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3630 views