Variable Clustering via Distributionally Robust Nodewise Regression
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Abstract (arXiv)
We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To solve the latter problem, we derive a convex relaxation, provide a data-driven approach for selecting the size of the robust region, and develop an ADMM algorithm for efficient implementation. We validate our method in extensive numerical studies and demonstrate its superior performance.
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References · 10
- 2013Intriguing properties of neural networkscited by 5,114
- 2019Distributionally Robust Optimization: A Reviewcited by 75
- 2015Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuitcited by 50
- 2016Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clusteringcited by 40
- 2016Practical sketching algorithms for low-rank matrix approximationcited by 38
- 2013Robust subspace clusteringcited by 24
- 2013Noisy Sparse Subspace Clusteringcited by 18
- 2016Parsimonious modeling with Information Filtering Networkscited by 14
- 2016The multiplex dependency structure of financial marketscited by 3
- 2014Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methodscited by 2
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