Quaternion-based deep belief networks fine-tuning

Papa, João Paulo, Rosa, Gustavo H., Pereira, Danillo R. and Yang, Xin-She ORCID logoORCID: https://orcid.org/0000-0001-8231-5556 (2017) Quaternion-based deep belief networks fine-tuning. Applied Soft Computing, 60 . pp. 328-335. ISSN 1568-4946 [Article] (doi:10.1016/j.asoc.2017.06.046)

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Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 22275
Depositing User: Xin-She Yang
Date Deposited: 24 Jul 2017 15:54
Last Modified: 29 Nov 2022 20:26
URI: https://eprints.mdx.ac.uk/id/eprint/22275

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