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JMLR
2010
136views more  JMLR 2010»
8 years 3 days ago
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by "sparse" matrices. Taking advantage of the c...
Ming Yuan
CORR
2010
Springer
228views Education» more  CORR 2010»
8 years 3 months ago
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
UAI
2008
8 years 6 months ago
Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
John Duchi, Stephen Gould, Daphne Koller
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
9 years 2 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
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