Unsupervised Feature Ranking via Attribute Networks
This addresses the need for identifying important features in unlabeled data, with applications in biological experiments and recommender systems, representing an incremental improvement over existing methods.
The paper tackles the problem of unsupervised feature ranking in unlabeled data by proposing FRANe, an algorithm based on network reconstruction and analysis. It demonstrates that FRANe outperforms state-of-the-art competitors on benchmarks and provides scalability analysis and interpretable relational structures.
The need for learning from unlabeled data is increasing in contemporary machine learning. Methods for unsupervised feature ranking, which identify the most important features in such data are thus gaining attention, and so are their applications in studying high throughput biological experiments or user bases for recommender systems. We propose FRANe (Feature Ranking via Attribute Networks), an unsupervised algorithm capable of finding key features in given unlabeled data set. FRANe is based on ideas from network reconstruction and network analysis. FRANe performs better than state-of-the-art competitors, as we empirically demonstrate on a large collection of benchmarks. Moreover, we provide the time complexity analysis of FRANe further demonstrating its scalability. Finally, FRANe offers as the result the interpretable relational structures used to derive the feature importances.