Dimensionality Reduction through Sub-space Mapping for Nearest Neighbor Algorithms

Authors:
Terry R. Payne;Peter Edwards
Affiliations:
-;-
Venue:
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Year:
2000

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Abstract

Many learning algorithms make an implicit assumption that all the attributes present in the data are relevant to a learning task. However, several studies have demonstrated that this assumption rarely holds; for many supervised learning algorithms, the inclusion of irrelevant or redundant attributes can result in a degradation in classification accuracy. While a variety of different methods for dimensionality reduction exist, many of these are only appropriate for datasets which contain a small number of attributes (e.g.