Spectral Self-supervised Feature Selection
This work addresses the challenge of selecting meaningful features for downstream tasks like clustering in unsupervised settings, with potential applications in domains such as biology, but it appears incremental as it builds on existing graph-based and self-supervised techniques.
The paper tackles the problem of unsupervised feature selection in high-dimensional data by proposing a self-supervised graph-based method that computes pseudo-labels from graph Laplacian eigenvectors and uses a surrogate model to predict them, demonstrating robustness to outliers and complex substructures in experiments on real-world datasets, particularly biological ones.
Choosing a meaningful subset of features from high-dimensional observations in unsupervised settings can greatly enhance the accuracy of downstream analysis, such as clustering or dimensionality reduction, and provide valuable insights into the sources of heterogeneity in a given dataset. In this paper, we propose a self-supervised graph-based approach for unsupervised feature selection. Our method's core involves computing robust pseudo-labels by applying simple processing steps to the graph Laplacian's eigenvectors. The subset of eigenvectors used for computing pseudo-labels is chosen based on a model stability criterion. We then measure the importance of each feature by training a surrogate model to predict the pseudo-labels from the observations. Our approach is shown to be robust to challenging scenarios, such as the presence of outliers and complex substructures. We demonstrate the effectiveness of our method through experiments on real-world datasets, showing its robustness across multiple domains, particularly its effectiveness on biological datasets.