Summary
In automatic pattern recognition applications, numerous features that describe the classes are obtained in an attempt to ensure accurate classification of unknown observations. These features or dimensions must be reduced to a smaller number before classification schemes can be applied, because classifiers become computationally and analytically unmanageable in high dimensions. Principal components and Fisher’s Linear Discriminant offer global dimensionality reduction within the framework of linear algebra applied to covariance matrices. This report describes local methods that use both mixture models and nearest neighbor calculations to construct local versions of these methods. These new versions for local dimensionality reduction will provide increased classification accuracy in lower dimensions.
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Acknowledgments
The authors would like to thank the reviewers and the editor for their helpful comments. Their work was supported by the In-house Laboratory Independent Research Program of the Naval Surface Warfare Center, Dahlgren Division.
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Marchette, D.J., Poston, W.L. Local dimensionality reduction. Computational Statistics 14, 469–489 (1999). https://doi.org/10.1007/s001800050026
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DOI: https://doi.org/10.1007/s001800050026