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 Videos · 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.

Videos

Lectures

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52:49

Some aspects of Latent Structure Analysis

Mike Titterington

Feb 25, 2007

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8133 views

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31:56

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

Janez Brank

Feb 25, 2007

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3445 views

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33:05

Auxillary Variational Information Maximization for Dimensionality Reduction

David Barber

Feb 25, 2007

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4643 views

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12:04

Online feature selection for contextual time series data

Petteri Nurmi

Feb 25, 2007

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3531 views

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21:44

Random projection, margins, kernels, and feature-selection

Avrim Blum

Feb 25, 2007

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7694 views

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53:22

Dimensionality Reduction by Feature Selection in Machine Learning

Dunja Mladenić

Feb 25, 2007

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17291 views

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24:32

A simple feature extraction for high dimensional image representations

Amit Gruber

Feb 25, 2007

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5250 views

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22:50

Constructing visual models with a latent space approach

Florent Monay

Feb 25, 2007

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3084 views

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19:51

A statistical learning approach to subspace identification of dynamical systems

Tijl De Bie

Feb 25, 2007

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6743 views

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23:09

Classification of high dimensional data: High Dimensional Discriminant Analysis

Charles Bouveyron

Feb 25, 2007

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4701 views

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22:44

Semantic text features from small world graphs

Jure Leskovec

Feb 25, 2007

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6559 views

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26:22

Identifying Feature Relevance using a Random Forest

Jeremy D. Rogers

Feb 25, 2007

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12620 views

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25:34

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

Amir Navot

Feb 25, 2007

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6908 views

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24:32

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

Andreas Maurer

Feb 25, 2007

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3445 views

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40:19

Discrete PCA

Wray Buntine

Feb 25, 2007

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7197 views

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58:39

Latent Semantic Variable Models

Thomas Hofmann

Feb 25, 2007

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29761 views

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21:35

Modelling Intra-Speaker Variability for Improved Speaker Recognition

Hagai Aronowitz

Feb 25, 2007

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4783 views

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14:59

Dimensionality Reduction in Gaussian Process Models

Lehel Csato

Feb 25, 2007

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4980 views

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25:35

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

Motoaki Kawanabe

Feb 25, 2007

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4505 views

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34:58

Greedy Feature Grouping for Optimal Discriminant Subspaces

Mahesan Niranjan

Feb 25, 2007

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3630 views