Weighted maximum variance dimensionality reduction

Dimensionality reduction procedures such as principal component analysis and the maximum margin criterion discriminant are special cases of a weighted maximum variance (WMV) approach. We present a simple two parameter version of WMV that we call 2P-WMV. We study the classification error given by the 1-nearest neighbor algorithm on features extracted by our and other dimensionality reduction methods on several real datasets. Our results show that our method yields the lowest average error across the datasets with statistical significance.

Contact: usman@cs.njit.edu
Errata: In the experimental results we define the error to be the number of miclassified points divided by total in the test set. The published paper incorrectly states that we used the balance error. The PDF below is the corrected version.

Citation: Turki Turki and Usman Roshan, Weighted maximum variance dimensionality reduction, To appear in Proceedings of the 6th Mexican Conference on Pattern Recognition, Cancun, Mexico, 2014 (PDF)