Discriminative and regularized echo state network for time series classification

Highlights：

• To enhance the stability of the output weights, an ORW algorithm is proposed based on the training errors for classification tasks. The training errors are then used to iterate backwards to train the loss function and regularization term for obtaining a more compact output layer architecture.

• The performance of our DR-ESN classifier on massive benchmarks indicate that the proposed DR-ESN can considerably improve the original ESN classifier and that our DR-ESN classifier yields comparable performance compared with some state-of-the-art classifiers. The input weights and output weights visualization results demonstrate the superiority of DR-ESN.

摘要

•We propose a DFA algorithm which uses the linear combinations of input samples features to regenerate the input weights of Echo State Network (ESN) instead of using the original random input weights. The input weights of our final ESN classifiers take the relative importance of temporal data at all classes into consideration.•To enhance the stability of the output weights, an ORW algorithm is proposed based on the training errors for classification tasks. The training errors are then used to iterate backwards to train the loss function and regularization term for obtaining a more compact output layer architecture.•The performance of our DR-ESN classifier on massive benchmarks indicate that the proposed DR-ESN can considerably improve the original ESN classifier and that our DR-ESN classifier yields comparable performance compared with some state-of-the-art classifiers. The input weights and output weights visualization results demonstrate the superiority of DR-ESN.