@inproceedings{060e9eaf3c9244e6b1a7f447bd00b32f,
title = "A solver of Fukunaga koontz transformation without matrix decomposition",
abstract = "Fukunaga Koontz Transformation provides a powerful tool for extracting discriminant subspaces in pattern classification. The discriminant subspaces are generally extracted by a matrix decomposition procedure involving scatter matrices where a nontrivial singularity problem is inevitable when sample number is limited. In this work, instead of matrix decomposition, a novel subspace extraction procedure based on solving a set of least-norm equations is proposed. This subspace extraction procedure does not rely on a large sample number and its computational complexity is only related to the number of samples. Experiments based on benchmark MNIST and PIE face recognition datasets show a promising potential of using the proposed method for certain image based recognition application where the image size is large while the sample number is limited.",
keywords = "Binary classification, Face recognition, Fukunaga koontz transformation, Subspace analysis",
author = "Hao Su and Jie Yang and Lei Sun and Zhiping Lin",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 ; Conference date: 22-05-2021 Through 28-05-2021",
year = "2021",
doi = "10.1109/ISCAS51556.2021.9401365",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings",
address = "United States",
}