@inproceedings{cb7b36017b1849979ef5c7c7939ac4d8,
title = "Semi-supervised Subspace Learning Via Constrained Matrix Factorization",
abstract = "This paper adopts the matrix factorization approach by improving the NMF model to build a semi-supervised learning framework (DCNMF) that integrates the linear discriminate analysis (LDA) and base cone volume constraints. The proposed DCNMF is a subspace learning model in which minimizing the basic cone volume of the learned subspace and reducing the data dimensionality so that the distance within-class samples is minimized and the distance between-class samples is maximized. The proposed method is evaluated by experiments on two cases of face recognition tasks, namely, various numbers of training data and different subspace dimensionalities.",
keywords = "data representation, face recognition, matrix factorization, subspace learning",
author = "Duong, {Viet Hang} and Bui, {Manh Quan} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd International Conference on Sustainable Technologies for Industry 4.0, STI 2021 ; Conference date: 18-12-2021 Through 19-12-2021",
year = "2021",
doi = "10.1109/STI53101.2021.9732540",
language = "???core.languages.en_GB???",
series = "2021 3rd International Conference on Sustainable Technologies for Industry 4.0, STI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 3rd International Conference on Sustainable Technologies for Industry 4.0, STI 2021",
}