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Efficient Latent Variable Graphical Model Selection via Split Bregman Method

We consider the problem of covariance matrix estimation in the presence of latent variables. Under suitable conditions, it is possible to learn the marginal covariance matrix of the observed variables via a tractable convex program, where the concentration matrix of the observed variables is decomposed into a sparse matrix (representing the graphical structure of the observed variables) and a low rank matrix (representing the marginalization effect of latent variables). We present an efficient first-order method based on split Bregman to solve the convex problem. The algorithm is guaranteed to converge under mild conditions. We show that our algorithm is significantly faster than the state-of-the-art algorithm on both artificial and real-world data. Applying the algorithm to a gene expression data involving thousands of genes, we show that most of the correlation between observed variables can be explained by only a few dozen latent factors.


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1 Introduction

Estimating covariance matrices in the high-dimensional setting arises in many applications and has drawn considerable interest recently. Because the sample covariance matrix is typically poorly behaved in the high-dimensional regime, regularizing the sample covariance based on some assumptions of the underlying true covariance is often essential to gain robustness and stability of the estimation.

One form of regularization that has gained popularity recently is to require the the underlying inverse covariance matrix to be sparse [1, 2, 3, 4]

. If the data follow a multivariate Gaussian distribution with covariance matrix

, the entries of the inverse covariance matrix (also known as concentration matrix or precision matrix) encode the information of conditional dependencies between variables: if the variables and are conditionally independent given all others. Therefore, the sparsity regularization is equivalent to the assumption that most of the variable pairs in the high-dimensional setting are conditionally independent.

To make the estimation problem computational tractable, one often adopts a convex relaxation of the sparsity constraint and uses the norm to promote the sparsity of the concentration matrix [3, 4, 5, 6]. Denote the empirical covariance. Under the maximum likelihood framework, the covariance matrix estimation problem is then formulated as solving the following optimization problem:


where tr denotes the trace, is a sparsity regularization parameter, and denotes that is positive semidefinite. Due to the penalty term and the explicit positive definite constraint on , the method leads to a sparse estimation of the concentration matrix that is guaranteed to be positive definite. The problem is convex and many algorithms have been proposed to solve the problem efficiently in high dimension [4, 7, 8, 9].

However, in many real applications only a subset of the variables are directly observed, and no additional information is provided on both the number of the latent variables and their relationship with the observed ones. For instance, in the area of functional genomics it is often the case that only mRNAs of the genes can be directly measured, but not the proteins, which are correlated but have no direct correspondence to the mRNAs because of the prominent role of the postranscriptional regulation. Another example is the movie recommender system where the preference of a movie can be strongly influenced by latent factors such as advertisements, social environment, etc. In these and other cases, the observed variables can be densely correlated because of the marginalization over the unobserved hidden variables. Therefore, the sparsity regularization alone may fail to achieve the desire results.

We consider the setting in which the hidden () and the observed variables () are jointly Gaussian with covariance matrix . The marginal statistics of the observed variable are given by the marginal covariance matrix , which is simply a submatrix of the full covariance matrix . Let the concentration matrix . The marginal concentration matrix corresponding to the observed variables is given by the Schur complement [10]:


where , and are the corresponding submatrices of the full concentration matrix. Based on the Schur complement, it is clear that the marginal concentration matrix of the observed variables can be decomposed into two components: one is , which specifies the conditional dependencies of the observed variables given both the observed and latent variables, and the other is , which represents the effect of marginalization over the hidden variables. One can now impose assumptions to the two underlying components separately.

By assuming that the matrix is sparse and the number of latent variables is small, the maximum likelihood estimation of the covariance matrix of the observed variables at the presence of latent variables can then be formulated as


where we decompose with denoting and denoting . Because the number of the hidden variables is small, is of low rank, whose convex relaxation is the trace norm. There are two regularization parameters in this model: regularizes the sparsity of , and regularizes the rank of . Under certain regularity conditions, Chandrasekaran et al. showed that this model can consistently estimate the underlying model structure in the high-dimensional regime in which the number of observed/hidden variables grow with the number of samples of the observed variables [10].

The objective function in (1) is strictly convex, so a global optimal solution is guaranteed to exist and be unique. Finding the optimal solution in the high-dimension setting is computationally challenging due to the term appeared in the likelihood function, the trace norm, the nondifferentiability of the penalty, and the positive semidefinite constraints. For large-scale problems, the state-of-the-art algorithm for solving (1) is to use the special purpose solver LogdetPPA [11] developed for log-determinant semidefinite programs. However, the solver LogdetPPA is designed to solve smooth problems. In order to use LogdetPPA, one has to reformulate (1) to a smooth problem. As a result, no optimal sparse matrix

can be generated and additional heuristic steps involving thresholding have to be applied in order to produce sparsity. In addition, LogdetPPA is not especially designed for (

1). We believe much more efficient algorithms can be generated by considering the unique structures of the model specifically.

The main contribution of this paper contains two aspects. First, we present a new algorithm for solving (1) and show that the algorithm is significantly faster than the state-of-the-art method, especially for large-scale problems. The algorithm is derived by reformulating the problem and adapting the split Bregman method [8, 9]. We derive closed form solutions for each subproblem involved in the split Bregman iterations. Second, we apply the method to analyze a large-scale gene expression data, and find that the model with latent variables explain the data much better than the one without assuming latent variables. In addition, we find that most of the correlations between genes can be explained by only a few latent factors, which provides a new aspect for analyzing this type of data.

The rest of the paper is organized as follows. In Section 2, we derive a split Bregman method, called SBLVGG, to solve the latent variable graphical model selection problem (1). The convergence property of the algorithm is also given. SBLVGG consists of four update steps and each update has explicit formulas to calculate. In Section 3, we illustrate the utility of our algorithm and compare its performance to LogdetPPA using both simulated data and gene expression data.

2 Split Bregman method for latent variable graphical model selection

The split Bregman method was originally proposed by Osher and coauthors to solve total variation based image restoration problems [12]. It was later found to be either equivalent or closely related to a number of other existing optimization algorithms, including Douglas-Rachford splitting [13], the alternating direction method of multipliers (ADMM) [14, 15, 12] and the method of multipliers [16]. Because of its fast convergence and the easiness of implementation, it is increasingly becoming a method of choice for solving large-scale sparsity recovery problems [17, 18]. Recently, it is also used to solve (1) and find it is very successful [8, 9].

In this section, we first reformulate the problem by introducing an auxiliary variable and then proceed to derive a split Bregman method to solve the reformulated problem. Here we would like to emphasize that, although split Bregman method has been introduced to solve graphical model problems [8, 9], we have our own contributions. Firstly, it is our first time to use split Bregman method to solve (1) and we introduce an auxiliary variable for a data fitting term instead of penalty term which has been adopted in [8, 9]. Secondly, Secondly, the update three hasn’t been appeared in [8, 9] and we provide an explicit formula for it as well. Thirdly, instead of using eig (or schur) decomposition as done in previous work [8, 9]

, we use the LAPACK routine dsyevd.f (based on a divide-and-conquer strategy) to compute the full eigenvalue decomposition of a symmetric matrix which is essential for updating the first and third subproblems.

2.1 Derivation of the split bregman method for latent variable graphical model selection

The log-likelihood term and the regularization terms in (1) are coupled, which makes the optimization problem difficult to solve. However, the three terms can be decoupled if we introduce an auxiliary variable to transfer the coupling from the objective function to the constraints. More specially, the problem (1) is equivalent to the following problem


The introduction of the new variable of is a key step of our algorithm, which makes the problem amenable to a split Bregman procedure to be detailed below. Although the split Bregman method originated from Bregman iterations, it has been demonstrated to be equivalent to the alternating direction method of multipliers (ADMM) [14, 15, 19]. For simplicity of presentation, next we derive the split Bregman method using the augmented Lagrangian method [20, 16].

We first define an augmented Lagrangian function of (4)


where is a dual variable matrix corresponding to the equality constraint , and is a parameter. Compared with the standard Lagrangian function, the augmented Lagrangian function has an extra term , which penalizes the violation of the linear constraint .

With the definition of the augmented Lagrangian function (5), the primal problem (4) is equivalent to


Exchanging the order of and in (6) leads to the formulation of the dual problem


Note that the gradient can be calculated by the following [21]


where .

Applying gradient ascent on the dual problem (7) and using equation (8), we obtain the method of multipliers [16] to solve (4)


Here we have used as the step size of the gradient ascent. It is easy to see that the efficiency of the iterative algorithm (9) largely hinges on whether the first equation of (9) can be solved efficiently. Note that the augmented Lagrangian function still contains and can not easily be solved directly. But we can solve the first equation of (9) through an iterative algorithm that alternates between the minimization of and . The method of multipliers requires that the alternative minimization of and are run multiple times until convergence to get the solution . However, because the first equation of (9) represents only one step of the overall iteration, it is actually not necessary to be solved completely. In fact, the split Bregman method (or the alternating direction method of multipliers [14]) uses only one alternative iteration to get a very rough solution of (9), which leads to the following iterative algorithm for solving (4) after some reformulations,


2.1.1 Convergence

The convergence of the iteration (10) can be derived from the convergence theory of the alternating direction method of multipliers or the convergence theory of the split Bregman method [14, 22, 17].

Theorem 1.

Let be generated by (10), and be the unique minimizer of (4). Then,

From Theorem 1, the condition for the convergence of the iteration (10) is quite mild and even irrelevant to the choice of the parameter in the iteration (10).

2.1.2 Explicit formulas to update and

We first focus on the computation of the first equation of (10). Taking the derivative of the objective function and setting it to be zero, we get


It is a quadratic equation where the unknown is a matrix. The complexity for solving this equation is at least because of the inversion involved in (11). Note that and , if is symmetric, so is . It is easy to check that the explicit form for the solution of (11) under constraint , i.e., , is


where and denotes the square root of a symmetric positive definite matrix . Recall that the square root of a symmetric positive definite matrix

is defined to be the matrix whose eigenvectors are the same as those of

and eigenvalues are the square root of those of . Therefore, to get the update of , can first compute the eigenvalues and eigenvectors of , and then get the eigenvalues of according to (12) by replacing the matrices by the corresponding eigenvalues. We adopt the LAPACK routine dsyevd.f (based on a divide-and-conquer strategy) to compute the full eigenvalue decomposition of . It is about times faster than eig (or schur) routine when is larger than .

For the second equation of (10), we have made the data fitting term separable with respect to the entries of . Thus, it is very easy to get the solution and the computational complexity would be for is also separable. Let be a soft thresholding operator defined on matrix space and satisfying

where . Then the update of is

For the update of , it can use the following Theorem.

Theorem 2.

Given a symmetric matrix and . Denote

Then , where are the eigenvalues of with being the corresponding eigenvector matrix and .


Note that where

is the identity matrix. Thus,

Compute eigenvalue decomposition on matrix and get , where and is the diagonal matrix. Then

Together with the fact that , should satisfy for and otherwise. Therefore, .      

Using the operator defined in Theorem 2, it is easy to see that


Here we also use the LAPACK routine dsyevd.f (based on a divide-and-conquer strategy) to compute the full eigenvalue decomposition of . Summarizing all together, we get SBLVGG to solve the latent variable Gaussian Graphical Model (1) as shown in Algorithm 1. [htb] Split Bregman method for solving Latent Variable Gaussian Graphical Model (SBLVGG)

  Initialize .
     1) , where

3 Numerical experiments

Next we illustrate the efficiency of the split Bregman method (SBLVGG) for solving (1) using time trials on artificial data as well as gene expression data. All the algorithms were implemented in Matlab and run on a 64-bit linux desktop with Intel i3 - 3.2GHz QuadCore CPU and 8GB memory. To evaluate the performance of SBLVGG, we compare it with logdetPPA [11] which is state-of-art solver for (1) in large-scale case. LogdetPPA was originally developed for log-determinant semidefinite programs with smooth penalties. In order to solve (1) using LogdetPPA , we need to reformulate (1) as a smooth problem as done in [10], which results in the derived sparse matrix not strictly sparse with many entries close to but not exactly . We also demonstrate that latent variable Gaussian graphical selection model (1) is better than sparse Gaussian graphical model (1) in terms of generalization ability using gene expression data.

Note that the convergence of Algorithm LABEL:algorithm_SBLVGG is guaranteed no matter what values of is used as shown in Theorem 1. The speed of the algorithm can, however, be influenced by the choices of as it would affect the number of iterations involved. In our implementation, we choose in for artificial data and for gene expression data.

3.1 Artificial data

Let with being the total number of variables in the graph, the number of observed variables and the number of hidden variables. The synthetic data are generated in a similar way as the one in Section 6.1 of [11]. First, we generate an random sparse matrix

with non-zero entries drawn from normal distribution

. Then set

Figure 1: (a) Comparison of CPU time curve w.r.t. number of variables for artificial data; (b) Comparison of CPU time curve w.r.t. number of variables for gene expression data
() Method Obj. Value Rank Sparse Ratio
(0.0025, 0.21) SBLVGG -5642.6678 8 5.56%
lodgetPPA -5642.6680 8 99.97%
(0.0025, 0.22) SBLVGG -5642.4894 3 5.58%
lodgetPPA -5642.4895 3 99.97%
(0.0027, 0.21) SBLVGG -5619.2744 16 4.14%
lodgetPPA -5619.2746 16 99.97%
(0.0027, 0.22) SBLVGG -5619.0194 6 4.17%
lodgetPPA -5619.0196 6 99.97%
Table 1: Numerical comparison at for artificial data

Note that is marginal precision matrix of observed variables. We generate Gaussian random samples from , and calculates its sample covariance matrix . In our numerical experiments, we set sparse ratio of around , and . The stopping criteria for SBLVGG is specified as follows. Let . We stop our algorithm if and with .

Figure 1(a) shows CPU time curve of SBLVGG and LogdetPPA with respect to the number of variable for the artificial data. For each fixed , the CPU time is averaged over 4 runs with four different pairs. We can see SBLVGG consistently outperform LogdetPPA. For dimension of 2500 or less, it is 3.5 times faster on average. For dimension 3000, it is 4.5 times faster. This also shows SBLVGG scales better to problem size than LogdetPPA. In terms of accuracy, Table 1 summarize performance of two algorithms at , in three aspects: objective value, rank of , sparsity of (ratio of non-zero off-diagonal elements). We find in terms of objective value and rank, both algorithms generate almost identical results. However, SBLVGG outperform LogdetPPA due to its soft- thresholding operator in Algorithm LABEL:algorithm_SBLVGG for , while LogdetPPA misses this kind of operator and result in many nonzero but close to zero entries due to numerical error. We would like to emphasize that the results in lower dimensions are very similar to , . We omit the details here due space limitation.

3.2 Gene expression data

The gene expression data [23] contains measurements of mRNA expression level of the genes of S. cerevisiae (yeast) under different experimental conditions. First we centralize the data and choose three subset of the data, , and

genes with highest variances. Figure

1(b) shows CPU time of SBLVGG and LogdetPPA with different . We can see that SBLVGG consistently perform better than LogdetPPA: in 1000

() Algorithm Obj. Value Rank # Non-0 Entries
(0.01,0.05) SBLVGG -9793.3451 88 34
LodgetPPA -9793.3452 88 8997000
(0.01,0.1) SBLVGG -9607.8482 60 134
LodgetPPA -9607.8483 60 8997000
(0.02,0.05) SBLVGG -8096.2115 79 0
LodgetPPA -8096.2115 79 8996998
(0.02,0.1) SBLVGG -8000.9047 56 0
LodgetPPA -8000.9045 56 8997000
Table 2: Numerical comparison at 3000 dimensional subset of gene expression data

dimension case, SBLVGG is times faster, while in 2000 and 3000 dimension case, almost 3 times faster. Table 2 summarize the accuracy for dimension case in three aspects: objective value, rank of , sparsity of (Number of non-zero off-diagonal elements) for four fixed pair of . Similar to artificial data, SBLVGG and LogdetPPA generate identical results in terms of objective value and number of hidden units. However, logdetPPA suffers from the floating point problem of not being able to generate exact sparse matrix. On the other hand, SBLVGG is doing much better in this aspect.

Exp. Number LVGG SGG
Rank of Sparsity of Sparsity of
1 48 30 -2191.3 24734 -1728.8
2 47 64 -2322.7 28438 -1994.1
3 50 58 -2669.9 35198 -2526.3
4 52 64 -2534.6 30768 -2282.5
5 48 0 -2924.0 29880 -2841.4
6 51 52 -2707.1 28754 -2642.6
7 45 0 -2873.3 30374 -2801.4
8 49 0 -2765.5 31884 -2536.7
9 48 54 -2352.0 29752 -2087.2
10 47 0 -2922.9 29760 -2843.5
Table 3: Comparison of generalization ability on gene expression data at dimension of 1000 using latent variable Gaussian graphical model (LVGG) and sparse Gaussian graphical model (SGG)

We also investigated generalization ability of latent variable Gaussian graphical selection model (1) versus sparse Gaussian graphical model (1) using this data set. A subset of the data, 1000 genes with highest variances, are used for this experiment. The 300 samples are randomly divided into 200 for training and 100 for testing. Denote the negative log likelihood (up to a constant difference)

where is the empirical covariance matrix using observed sample data and is the estimated covariance matrix based on model (1) or model (1). It easy to see that is equivalent to negative Log-likelihood function up to some scaling. Therefore, we use as a criteria for cross-validation or prediction. Regularization parameters for model (1) and for model (1) are selected by 10-fold cross validation on training set. Table 3 shows that latent variable Gaussian graphical selection model (1) consistently outperform sparse Gaussian graphical model (1) in terms of generalization ability using criteria . We also note that latent variable Gaussian graphical selection model (1) tend to use moderate number of hidden units, and very sparse conditional correlation to explain the data. For , it tend to predict about hidden units, and the number of direct interconnections between observed variables are tens, and sometimes even 0. This suggests that most of the correlations between genes observed in the mRNA measurement can be explained by only a small number of latent factors. Currently we only tested the generalization ability of latent variable Gaussian graphical selection model using . The initial result with gene expression data is encouraging. Further work (model selection and validation) will be done by incorporating other prior information or by comparing with some known gene interactions.

4 Discussion

Graphical model selection in high-dimension arises in a wide range of applications. It is common that in many of these applications, only a subset of the variables are directly observable. Under this scenario, the marginal concentration matrix of the observed variables is generally not sparse due to the marginalization of latent variables. A computational attractive approach is to decompose the marginal concentration matrix into a sparse matrix and a low-rank matrix, which reveals the conditional graphical model structure in the observed variables as well as the number of and effect due to the hidden variables. Solving the regularized maximum likelihood problem is however nontrivial for large-scale problems, because of the complexity of the log-likelihood term, the trace norm penalty and norm penalty. In this work, we propose a new approach based on the split Bregman method (SBLVGG) to solve it. We show that our algorithm is at least three times faster than the state-of-art solver for large-scale problems.

We applied the method to analyze the expression of genes in yeast in a dataset consisting of thousands of genes measured over 300 different experimental conditions. It is interesting to note that the model considering the latent variables consistently outperforms the one without considering latent variables in term of testing likelihood. We also note that most of the correlations observed between mRNAs can be explained by only a few dozen latent variables. The observation is consistent with the module network idea proposed in the genomics community. It also might suggest that the postranscriptional regulation might play more prominent role than previously appreciated.


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