Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161793
Title: Representation learning using deep random vector functional link networks for clustering
Authors: Hu, Minghui
Suganthan, Ponnuthurai Nagaratnam
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Hu, M. & Suganthan, P. N. (2022). Representation learning using deep random vector functional link networks for clustering. Pattern Recognition, 129, 108744-. https://dx.doi.org/10.1016/j.patcog.2022.108744
Journal: Pattern Recognition 
Abstract: Random Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised learning, including semi-supervised and multi-label. There are hardly any unsupervised research results for RVFLs. In this paper, we propose the unsupervised RVFL (usRVFL), and the unsupervised framework is generic that can be used with other RVFL variants, thus we extend it to an ensemble deep variant, unsupervised deep RVFL (usdRVFL). The unsupervised method is based on the manifold regularization while the deep variant is related to the consensus clustering method, which can increase the capability and diversity of RVFLs. Our unsupervised approaches also benefit from fast training speed, even the deep variant offers a very competitive computation efficiency. Empirical experiments on several benchmark datasets demonstrated the effectiveness of the proposed algorithms.
URI: https://hdl.handle.net/10356/161793
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2022.108744
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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