Goodman, S. and Hunter, Andrew (1999) Feature extraction algorithms for pattern classification. In: 9th International Conference on Neural Networks, 7-10 September 1999, University of Edinburgh, Edinburgh, Scotland.
|Item Type:||Conference or Workshop Item (Paper)|
|Divisions:||College of Science > School of Computer Science|
|Abstract:||Feature extraction is often an important preprocessing step in classifier design, in order to overcome the problems associated with having a large input space. A common way of doing this is to use principle component analysis to find the most important features. However, it has been recognised that this may not produce an optimal set of features in some problems since the method relies on the second order statistics (covariance structure) of the data. In the paper a method called projection pursuit is presented, which is capable of extracting features based on higher order statistics of the distribution. The original projection pursuit algorithm performs a full d-dimensional search (where d is the number of features sought) that is impractical when d is large. Instead, a simple stepwise approach is suggested in which the computations only grow linearly with d. Some simulations on six publicly available data sets are shown which shows how it may be superior to PCA on some tasks in pattern classification|
|Date Deposited:||09 Jul 2010 11:36|
Actions (login required)