TY - GEN
T1 - Comparison of Fourier Bases and Asymmetric Network Bases in the Bio-Inspired Networks
AU - Ishii, Naohiro
AU - Iwata, Kazunori
AU - Iwahori, Yuji
AU - Matsuo, Tokuro
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Machine learning, deep learning and neural networks are extensively developed in many fields, in which neural network architectures have shown a variety of applications. However, there is a need for explainable fundamentals in complex neural networks. In this paper, it is shown that bio-inspired networks are useful for the explanation of network functions. First, the asymmetric network is created based on the bio-inspired retinal network. They have orthogonal bases which correspond to the Fourier bases. Second, the classification performance of the asymmetric network is compared to the conventional symmetric network. Further, the asymmetric network is extended to the layered networks, which generate higher dimensional orthogonal bases. Their replacement operation is shown to be useful in the classification. These higher dimensional bases preserve the independence of patterns in their layered networks. Finally, it is shown that the sparse codes made of the higher dimensional bases are applied to the classification of real-world data.
AB - Machine learning, deep learning and neural networks are extensively developed in many fields, in which neural network architectures have shown a variety of applications. However, there is a need for explainable fundamentals in complex neural networks. In this paper, it is shown that bio-inspired networks are useful for the explanation of network functions. First, the asymmetric network is created based on the bio-inspired retinal network. They have orthogonal bases which correspond to the Fourier bases. Second, the classification performance of the asymmetric network is compared to the conventional symmetric network. Further, the asymmetric network is extended to the layered networks, which generate higher dimensional orthogonal bases. Their replacement operation is shown to be useful in the classification. These higher dimensional bases preserve the independence of patterns in their layered networks. Finally, it is shown that the sparse codes made of the higher dimensional bases are applied to the classification of real-world data.
KW - asymmetric and symmetric networks
KW - classification performance of networks
KW - generation of orthogonal bases
KW - independence in extended layered network
KW - replacement of bases
UR - https://www.scopus.com/pages/publications/85174522485
UR - https://www.scopus.com/pages/publications/85174522485#tab=citedBy
U2 - 10.1007/978-3-031-43085-5_16
DO - 10.1007/978-3-031-43085-5_16
M3 - Conference contribution
AN - SCOPUS:85174522485
SN - 9783031430848
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 200
EP - 210
BT - Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings
A2 - Rojas, Ignacio
A2 - Joya, Gonzalo
A2 - Catala, Andreu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023
Y2 - 19 June 2023 through 21 June 2023
ER -