Using Randomization to Break the Curse of Dimensionality

Using Randomization to Break the Curse of Dimensionality Author(s):

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One class random forests

One class classification is a binary classification task for which only one class of samples is available for learning. In some preliminary works, we have proposed One Class Random Forests (OCRF), a method based on a random forest algorithm and an original outlier generation procedure that makes use of classifier ensemble randomization principles. In this paper, we propose an extensive study of...

Several Approaches to Break the Curse of Dimensionality

Lecture 6 : Curse of Dimensionality , Dimension Reduction

High dimensional vectors appear frequently in recent development in CS, examples of which are user-movie ratings of netflix, DNA strings of patients, and images pixel values. In this lecture we study higher dimensions geometry and see how randomization help us to design algorithm. We first start with a few definitions, then we discuss several properties of high dimensional geometry, and finally...

Learning in high dimensions with projected linear discriminants

The enormous power of modern computers has made possible the statistical modelling of data with dimensionality that would have made this task inconceivable only decades ago. However, experience in such modelling has made researchers aware of many issues associated with working in high-dimensional domains, collectively known as ‘the curse of dimensionality’, which can confound practitioners’ des...