Double-dictionary learning unsupervised feature selection cooperating with low-rank and sparsity

The feature selection algorithm based on dictionary learning has been widely studied for its excellent interpretability. In the feature selection process, many algorithms only consider the global or local geometric structure information of the original data. A few algorithms that utilize the global and local information together do not actually use the two parts synchronously. Because of this, the information of the two parts cannot be fully utilized reasonably. For this reason, a novel feature selection algorithm, double-dictionary learning unsupervised feature selection cooperating with low-rank and sparsity (LRSDFS), is proposed in this paper. First, LRSDFS improves the traditional dictionary learning by synchronously reconstructing the original dataset into two dictionaries simultaneously. Second, the low-rank and sparsity constraint are applied to the two dictionaries, so that the reconstructed dictionary can retain the global and local information of the original data simultaneously. Finally, the global and local information are weighted to realize the feature selection of the dataset, making the selected features more reasonable and interpretable. LRSDFS is compared with seven state of the art algorithms, including baseline, and evaluated on nine publicly available benchmark datasets. The results show that LRSDFS is more efficient than other unsupervised feature selection algorithms.