A block-coordinate descent approach for large-scale sparse inverse covariance estimation

Eran Treister, Javier Turek

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


The sparse inverse covariance estimation problem arises in many statistical applications in machine learning and signal processing. In this problem, the inverse of a covariance matrix of a multivariate normal distribution is estimated, assuming that it is sparse. An l1 regularized log-determinant optimization problem is typically solved to approximate such matrices. Because of memory limitations, most existing algorithms are unable to handle large scale instances of this problem. In this paper we present a new block-coordinate descent approach for solving the problem for large-scale data sets. Our method treats the sought matrix block-by-block using quadratic approximations, and we show that this approach has advantages over existing methods in several aspects. Numerical experiments on both synthetic and real gene expression data demonstrate that our approach outperforms the existing state of the art methods, especially for large-scale problems.

Original languageEnglish
Pages (from-to)927-935
Number of pages9
JournalAdvances in Neural Information Processing Systems
Issue numberJanuary
StatePublished - 1 Jan 2014
Externally publishedYes
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: 8 Dec 201413 Dec 2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


Dive into the research topics of 'A block-coordinate descent approach for large-scale sparse inverse covariance estimation'. Together they form a unique fingerprint.

Cite this