Quaternion-based Deep Belief Networks fine-tuning

Author(s)

Papa, Joao Paulo ^[1] ; Rosa, Gustavo H. ^[1] ; Pereira, Danillo R. ^[1] ; Yang, Xin-She ^[2]

Affiliation

^[1] Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil

^[2] Middlesex Univ, Sch Sci & Technol, London NW4 4BT - England

• Document type: Journal article
• Source: APPLIED SOFT COMPUTING; v. 60, p. 328-335, NOV 2017.
• Web of Science Citations: 6

Abstract

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. (C) 2017 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/25739-4 - On the Study of Semantics in Deep Learning Models
• Grantee: Gustavo Henrique de Rosa
• Support Opportunities: Scholarships in Brazil - Master