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A Feature Selection Algorithm Capable of Handling Extremely Large Data Dimensionality

9 years 17 days ago
A Feature Selection Algorithm Capable of Handling Extremely Large Data Dimensionality
With the advent of high throughput technologies, feature selection has become increasingly important in a wide range of scientific disciplines. We propose a new feature selection algorithm that performs extremely well in the presence of a huge number of irrelevant features. The key idea is to decompose an arbitrarily complex nonlinear models into a set of locally linear ones through local learning, and then estimate feature relevance globally within a large margin framework. The algorithm is capable of processing many thousands of features within a few minutes on a personal computer, yet maintains a close-to-optimum accuracy that is nearly insensitive to a growing number of irrelevant features. Experiments on eight synthetic and real-world datasets are presented that demonstrate the effectiveness of the algorithm.
Yijun Sun, Sinisa Todorovic, Steve Goodison
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where SDM
Authors Yijun Sun, Sinisa Todorovic, Steve Goodison
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