TY - GEN
T1 - Weighted fisher discriminant analysis in the input and feature spaces
AU - Ghojogh, Benyamin
AU - Sikaroudi, Milad
AU - Tizhoosh, H. R.
AU - Karray, Fakhri
AU - Crowley, Mark
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.
AB - Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.
KW - Automatically weighted FDA
KW - Cosine-weighted FDA
KW - Fisher Discriminant Analysis (FDA)
KW - Kernel FDA
KW - Manually weighted FDA
UR - http://www.scopus.com/inward/record.url?scp=85087274497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087274497&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50516-5_1
DO - 10.1007/978-3-030-50516-5_1
M3 - Conference contribution
AN - SCOPUS:85087274497
SN - 9783030505158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 15
BT - Image Analysis and Recognition - 17th International Conference, ICIAR 2020, Proceedings
A2 - Campilho, Aurélio
A2 - Karray, Fakhri
A2 - Wang, Zhou
PB - Springer
T2 - 17th International Conference on Image Analysis and Recognition, ICIAR 2020
Y2 - 24 June 2020 through 26 June 2020
ER -