Weighted maximum variance dimensionality reduction
Abstract:
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.
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)