Volume 142, Number 5, June 2023
|Number of page(s)
|Statistical physics and networks
|30 May 2023
Deep time-recurrence features
Barts and The London School of Medicine and Dentistry, Queen Mary University of London Turner Street, London E1 2AD, UK
(a) E-mail: email@example.com (corresponding author)
Received: 22 March 2023
Accepted: 19 May 2023
In this study, the notion of recurrence in chaos and nonlinear dynamics is formulated as a new method for deep feature extraction from complex data. This feature type can enhance the learning power of a state-of-the-art approach in artificial intelligence using time series or sequential data. The deep time-recurrence features are derived using the fuzzy recurrence algorithm and the iterative procedure of convolution, non-linear transformation, and down-sampling. Experimental results obtained from using a public database of healthy and pathological voice signals show that the proposed features can significantly improve the classification performance of long short-term memory networks.
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