Dimension reduction in recurrent networks by canonicalization

• Main Authors: Grigoryeva, Lyudmila, Ortega, Juan-Pablo
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Mathematics; Recurrent Neural Network; Reservoir Computing
• Online Access: https://hdl.handle.net/10356/161577
• Institution: Nanyang Technological University

Many recurrent neural network machine learning paradigms can be formulated using state-space representations. The classical notion of canonical state-space realization is adapted in this paper to accommodate semi-infinite inputs so that it can be used as a dimension reduction tool in the recurrent n...