Dictionary Learning-Based Data Pruning for System Identification

In system identification, augmenting time series data via time shifting and nonlinearisation can lead to both feature and sample redundancy. However, research has mainly focused on feature redundancy while largely ignoring the issue of sample redundancy. This paper proposes a novel data pruning method, called mini-batch FastCan, to reduce sample-wise redundancy based on dictionary learning. Time series data is represented by some representative samples via dictionary learning. The useful samples are selected based on their correlation with the representative samples. The method is tested on two simulated datasets and two benchmark datasets. The R-squared value between the coefficients of models trained on the full datasets and the coefficients of models trained on pruned datasets is adopted to evaluate the performance of data pruning methods. It is found that the proposed method significantly outperforms the random pruning method, with a higher median or mean and a lower variance of R-squared values.