# Low-rank Dictionary Learning for Unsupervised Feature Selection

arXiv: https://arxiv.org/abs/2106.11102 · HTML: https://mlsanity.com/paper/2106.11102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Feature Selection using a Teacher-Student Network](https://mlsanity.com/paper/1903.07045/) (2019)
- [Robust Subspace Clustering with Compressed Data](https://mlsanity.com/paper/1803.11305/) (2018)
- [Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering](https://mlsanity.com/paper/1912.05458/) (2019)
- [Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier](https://mlsanity.com/paper/1905.11543/) (2019)
- [Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation](https://mlsanity.com/paper/1908.01266/) (2019)
- [Relevant based structure learning for feature selection](https://mlsanity.com/paper/1608.07934/) (2016)

## Abstract (arXiv)

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 ℓ2,1-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.