Sparse Inverse Covariance Matrix Estimation using Quadratic Approximation

Cho-Jui Hsieh, Mátyás Sustik, Inderjit Dhillon, Pradeep Ravikumar

Abstract:   The l1 regularized Gaussian maximum likelihood estimator has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel algorithmfor solving the resulting optimization problem which is a regularized log-determinant program. In contrast to other state-of-the-artmethods that largely use first order gradient information, our algorithm is based on Newton’s method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem. We show that our method is superlinearly convergent, and also present experimental results using synthetic and real application data that demonstrate the considerable improvements in performance of our method when compared to other state-of-the-art methods.

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  • Sparse Inverse Covariance Matrix Estimation using Quadratic Approximation (pdf)
    C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
    In Neural Information Processing Systems (NIPS), December 2011.


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