Compressed Covariance Estimation with Automated Dimension Learning | Academic Article individual record

© 2018 Indian Statistical Institute We propose a method for estimating a covariance matrix that can be represented as a sum of a low-rank matrix and a diagonal matrix. The proposed method compresses high-dimensional data, computes the sample covariance in the compressed space, and lifts it back to the ambient space via a decompression operation. A salient feature of our approach relative to existing literature on combining sparsity and low-rank structures in covariance matrix estimation is that we do not require the low-rank component to be sparse. A principled framework for estimating the compressed dimension using Stein’s Unbiased Risk Estimation theory is demonstrated. Experimental simulation results demonstrate the efficacy and scalability of our proposed approach.

author list (cited authors)
Sabnis, G., Pati, D., & Bhattacharya, A.
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Springer Nature Publisher
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