TY - GEN
T1 - Weighted maximum variance dimensionality reduction
AU - Turki, Turki
AU - Roshan, Usman
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - dimensionality reduction
KW - maximum margin criterion
KW - principal component analysis
UR - http://www.scopus.com/inward/record.url?scp=84904878069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904878069&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-07491-7_2
DO - 10.1007/978-3-319-07491-7_2
M3 - Conference contribution
AN - SCOPUS:84904878069
SN - 9783319074900
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 11
EP - 20
BT - Pattern Recognition - 6th Mexican Conference, MCPR 2014, Proceedings
PB - Springer Verlag
T2 - 6th Mexican Conference on Pattern Recognition, MCPR 2014
Y2 - 25 June 2014 through 28 June 2014
ER -