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An efficient Long Short-Term Memory model based on Laplacian Eigenmap in artificial neural networks
Journal article   Peer reviewed

An efficient Long Short-Term Memory model based on Laplacian Eigenmap in artificial neural networks

Fang Hu, Yanhui Zhu, Jia Liu and Liuhuan Li
Applied soft computing, Vol.91, 106218
06/2020
DOI: https://doi.org/10.1016/j.asoc.2020.106218
Web of Science ID: WOS:000535478600004

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11 Times Cited - Web of Science

Abstract

Laplacian Eigenmap Long Short-Term Memory Recurrent neural network Algorithms Artificial Intelligence or Cybernetics
A new algorithm for data prediction based on the Laplacian Eigenmap (LE) is presented. We construct the Long Short-Term Memory model with the application of the LE in artificial neural networks. The new Long Short-Term Memory model based on Laplacian Eigenmap (LE-LSTM) reserves the characteristics of original data using the eigenvectors derived from the Laplacian matrix of the data matrix. LE-LSTM introduces the projection layer embedding data into a lower dimension space so that it improves the efficiency. With the implementation of LE, LE-LSTM provides higher accuracy and less running time on various simulated data sets with characteristics of multivariate, sequential, and time-series. In comparison with previously reported algorithms such as stochastic gradient descent and artificial neural network with three layers, LE-LSTM leads to many more successful runs and learns much faster. The algorithm provides a computationally efficient approach to most of the artificial neural network data sets.

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