@inproceedings{88317e5c689d44359fad83972e13bd10,
title = "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.",
keywords = "dimensionality reduction, maximum margin criterion, principal component analysis",
author = "Turki Turki and Usman Roshan",
year = "2014",
doi = "10.1007/978-3-319-07491-7_2",
language = "American English",
isbn = "9783319074900",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "11--20",
booktitle = "Pattern Recognition - 6th Mexican Conference, MCPR 2014, Proceedings",
address = "Germany",
note = "6th Mexican Conference on Pattern Recognition, MCPR 2014 ; Conference date: 25-06-2014 Through 28-06-2014",
}