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119
Stability selection
"... Proofs subject to correction. Not to be reproduced without permission. Contributions to the discussion must not exceed 400 words. Contributions longer than 400 words will be cut by the editor. 1 2 ..."
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Cited by 64 (2 self)
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Proofs subject to correction. Not to be reproduced without permission. Contributions to the discussion must not exceed 400 words. Contributions longer than 400 words will be cut by the editor. 1 2
Covariance regularization by thresholding
, 2007
"... This paper considers regularizing a covariance matrix of p variables estimated from n observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm as long as the true covariance matrix is sparse in a suitable sense, the variables are Gaussian or subGa ..."
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Cited by 63 (9 self)
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This paper considers regularizing a covariance matrix of p variables estimated from n observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm as long as the true covariance matrix is sparse in a suitable sense, the variables are Gaussian or subGaussian, and (log p)/n → 0, and obtain explicit rates. The results are uniform over families of covariance matrices which satisfy a fairly natural notion of sparsity. We discuss an intuitive resampling scheme for threshold selection and prove a general crossvalidation result that justifies this approach. We also compare thresholding to other covariance estimators in simulations and on an example from climate data. 1. Introduction. Estimation
Estimation of (near) lowrank matrices with noise and highdimensional scaling
"... We study an instance of highdimensional statistical inference in which the goal is to use N noisy observations to estimate a matrix Θ ∗ ∈ R k×p that is assumed to be either exactly low rank, or “near ” lowrank, meaning that it can be wellapproximated by a matrix with low rank. We consider an Me ..."
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Cited by 35 (11 self)
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We study an instance of highdimensional statistical inference in which the goal is to use N noisy observations to estimate a matrix Θ ∗ ∈ R k×p that is assumed to be either exactly low rank, or “near ” lowrank, meaning that it can be wellapproximated by a matrix with low rank. We consider an Mestimator based on regularization by the traceornuclearnormovermatrices, andanalyze its performance under highdimensional scaling. We provide nonasymptotic bounds on the Frobenius norm error that hold for a generalclassofnoisyobservationmodels,and apply to both exactly lowrank and approximately lowrank matrices. We then illustrate their consequences for a number of specific learning models, including lowrank multivariate or multitask regression, system identification in vector autoregressive processes, and recovery of lowrank matrices from random projections. Simulations show excellent agreement with the highdimensional scaling of the error predicted by our theory. 1.
Highdimensional covariance estimation by minimizing ℓ1penalized logdeterminant divergence
, 2008
"... ..."
High dimensional analysis of semidefinite relaxations for sparse principal component analysis
, 2008
"... Principal component analysis (PCA) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance matrix. However, PCA is well known to behave poorly in the “large p, small n ” setting, in which the problem dimension p is comparable to or la ..."
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Cited by 31 (2 self)
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Principal component analysis (PCA) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance matrix. However, PCA is well known to behave poorly in the “large p, small n ” setting, in which the problem dimension p is comparable to or larger than the sample size n. This paper studies PCA in this highdimensional regime, but under the additional assumption that the maximal eigenvector is sparse, say with at most k nonzero components. We analyze two computationally tractable methods for recovering the support of this maximal eigenvector: (a) a simple diagonal cutoff method, which transitions from success to failure as a function of the order parameter θdia(n, p, k) = n/[k 2 log(p − k)]; and (b) a more sophisticated semidefinite programming (SDP) relaxation, which succeeds once the order parameter θsdp(n, p, k) = n/[k log(p − k)] is larger than a critical threshold. Our results thus highlight an interesting tradeoff between computational and statistical efficiency in highdimensional inference.
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
"... 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 algorithm f ..."
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Cited by 27 (4 self)
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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 algorithm for solving the resulting optimization problem which is a regularized logdeterminant program. In contrast to other stateoftheart methods 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 stateoftheart methods. 1
ESTIMATING TIMEVARYING NETWORKS
, 2010
"... Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward es ..."
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Cited by 25 (6 self)
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Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating timevarying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating timevarying networks, which both build on a temporally smoothed l1regularized logistic regression formalism that can be cast as a standard convexoptimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated timevarying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.
Model selection in gaussian graphical models: Highdimensional consistency of l1regularized MLE
, 2008
"... We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d. samples. We study the performance of study the performance of the ℓ1regularized maximum likelihood estimator in the highdimensional setting, where the number of nodes in the ..."
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Cited by 25 (1 self)
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We consider the problem of estimating the graph structure associated with a Gaussian Markov random field (GMRF) from i.i.d. samples. We study the performance of study the performance of the ℓ1regularized maximum likelihood estimator in the highdimensional setting, where the number of nodes in the graph p, the number of edges in the graph s and the maximum node degree d, are allowed to grow as a function of the number of samples n. Our main result provides sufficient conditions on (n, p, d) for the ℓ1regularized MLE estimator to recover all the edges of the graph with high probability. Under some conditions on the model covariance, we show that model selection can be achieved for sample sizes n = Ω(d 2 log(p)), with the error decaying as O(exp(−clog(p))) for some constant c. We illustrate our theoretical results via simulations and show good correspondences between the theoretical predictions and behavior in simulations. 1
Generalized thresholding of large covariance matrices
 J. Amer. Statist. Assoc. (Theory and Methods
, 2009
"... We propose a new class of generalized thresholding operators which combine thresholding with shrinkage, and study generalized thresholding of the sample covariance matrix in high dimensions. Generalized thresholding of the covariance matrix has good theoretical properties and carries almost no compu ..."
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Cited by 21 (2 self)
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We propose a new class of generalized thresholding operators which combine thresholding with shrinkage, and study generalized thresholding of the sample covariance matrix in high dimensions. Generalized thresholding of the covariance matrix has good theoretical properties and carries almost no computational burden. We obtain an explicit convergence rate in the operator norm that shows the tradeoff between the sparsity of the true model, dimension, and the sample size, and show that generalized thresholding is consistent over a large class of models as long as the dimension p and the sample size n satisfy log p/n → 0. In addition, we show
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 covariance matrix of the Gaussian distribution, one can learn the structure of the graph by estimating a sparse inverse co ..."
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Cited by 20 (1 self)
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Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse covariance matrix of the Gaussian distribution, one can learn the structure of the graph by estimating a sparse inverse covariance matrix from sample data, by solving a convex maximum likelihood problem with an ℓ1regularization term. In this paper, we propose a firstorder method based on an alternating linearization technique that exploits the problem’s special structure; in particular, the subproblems solved in each iteration have closedform solutions. Moreover, our algorithm obtains an ϵoptimal solution in O(1/ϵ) iterations. Numerical experiments on both synthetic and real data from gene association networks show that a practical version of this algorithm outperforms other competitive algorithms. 1