Low-rank Dictionary Learning for Unsupervised Feature Selection
This addresses the problem of efficient learning and model complexity reduction for unlabeled high-dimensional data in domains like biology and computer vision, but it is incremental as it builds on existing dictionary learning and spectral analysis techniques.
The paper tackles unsupervised feature selection for high-dimensional data by proposing a low-rank dictionary learning method that preserves feature correlations and sample similarities, and experimental results show it outperforms state-of-the-art algorithms on standard datasets.
There exist many high-dimensional data in real-world applications such as biology, computer vision, and social networks. Feature selection approaches are devised to confront with high-dimensional data challenges with the aim of efficient learning technologies as well as reduction of models complexity. Due to the hardship of labeling on these datasets, there are a variety of approaches on feature selection process in an unsupervised setting by considering some important characteristics of data. In this paper, we introduce a novel unsupervised feature selection approach by applying dictionary learning ideas in a low-rank representation. Dictionary learning in a low-rank representation not only enables us to provide a new representation, but it also maintains feature correlation. Then, spectral analysis is employed to preserve sample similarities. Finally, a unified objective function for unsupervised feature selection is proposed in a sparse way by an $\ell_{2,1}$-norm regularization. Furthermore, an efficient numerical algorithm is designed to solve the corresponding optimization problem. We demonstrate the performance of the proposed method based on a variety of standard datasets from different applied domains. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithm.