An efficient Long Short-Term Memory model based on Laplacian Eigenmap in artificial neural networks(SCI)
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项目简介：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.