1-Bit Matrix Completion under Exact Low-Rank Constraint

02/24/2015 ∙ by Sonia Bhaskar, et al. ∙ Stanford University 0

We consider the problem of noisy 1-bit matrix completion under an exact rank constraint on the true underlying matrix M^*. Instead of observing a subset of the noisy continuous-valued entries of a matrix M^*, we observe a subset of noisy 1-bit (or binary) measurements generated according to a probabilistic model. We consider constrained maximum likelihood estimation of M^*, under a constraint on the entry-wise infinity-norm of M^* and an exact rank constraint. This is in contrast to previous work which has used convex relaxations for the rank. We provide an upper bound on the matrix estimation error under this model. Compared to the existing results, our bound has faster convergence rate with matrix dimensions when the fraction of revealed 1-bit observations is fixed, independent of the matrix dimensions. We also propose an iterative algorithm for solving our nonconvex optimization with a certificate of global optimality of the limiting point. This algorithm is based on low rank factorization of M^*. We validate the method on synthetic and real data with improved performance over existing methods.



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I Introduction

The problem of recovering a low rank matrix from an incomplete or noisy sampling of its entries arises in a variety of applications, including collaborative filtering [1] and sensor network localization [2, 3]. In many applications, the observations are not only missing, but are also highly discretized, e.g. binary-valued (1-bit) [4, 5], or multiple-valued [6]. For example, in the Netflix problem where a subset of the users’ ratings is observed, the ratings take integer values between 1 and 5. Although one can apply existing matrix completion techniques to discrete-valued observations by treating them as continuous-valued, performance can be improved by treating the values as discrete [4].

In this paper we consider the problem of completing a matrix from a subset of its entries, where instead of observing continuous-valued entries, we observe a subset of 1-bit measurements. Given , a subset of indices , and a twice differentiable function

, we observe (“w.p.” stands for “with probability”)


One important application is the binary quantization of , where is a noise matrix with i.i.d entries. If we take

to be the cumulative distribution function of

, then the model in (1) is equivalent to observing


Recent work in the 1-bit matrix completion literature has followed the probabilistic model in (1)-(2) for the observed matrix and has estimated via solving a constrained maximum likelihood (ML) optimization problem. Under the assumption that is low-rank, these works have used convex relaxations for the rank via the trace norm [4] or max-norm [5]

. An upper bound on the matrix estimation error is given under the assumptions that the entries are sampled according to a uniform distribution

[4], or in [5], following a non-uniform distribution.

In this paper, we follow [4, 5] in seeking an ML estimate of but use an exact rank constraint on rather than a convex relaxation for the rank. We follow the sampling model of [7] for which includes the uniform sampling of [4] as well as non-uniform sampling. We provide an upperbound on the Frobenius norm of matrix estimation error, and show that our bound yields faster convergence rate with matrix dimensions than the existing results of [4, 5] when the fraction of revealed 1-bit observations is fixed independent of the matrix dimensions. Lastly, we present an iterative algorithm for solving our nonconvex optimization problem with a certificate of global optimality under mild conditions. Our algorithm outperforms [4, 5] in the presented simulation example.

Notation: For matrix with -th entry , we use the notation for the entry-wise infinity-norm, for the Frobenius norm and for its operator norm. We use to denote the -th row and to denote the -th column. Taking to be a set, we use to denote the cardinality of . The notation represents the set of integers . We denote by

the vector of all ones, by

the unit vector , and by the indicator function, i.e. when is true, else .

Ii Model Assumptions

We wish to estimate unknown using a constrained ML approach. We use to denote the optimization variable. Then the negative log-likelihood function for the given problem is


Note that (3) is a convex function of when the function is log-concave. Two common choices for which the function is log-concave are: Logit model with logistic function and parameter , or equivalently in (2) is logistic with scale parameter ; Probit model with where and is the cumulative distribution function of . We assume that is a low-rank matrix with rank bounded by , and that the true matrix satisfies , which helps make the recovery of well-posed by preventing excessive “spikiness” of the matrix. We refer the reader to [4, 5] for further details.

The constrained ML estimate of interest is the solution to the optimization problem (s.t.: subject to):


In many applications, such as sensor network localization, collaborative filtering, or DNA haplotype assembly, the rank is known or can be reliably estimated [8].

We now discuss our assumptions on the set . Consider a bipartite graph , where the edge set is related to the index set of revealed entries as iff . Abusing the notation, we use for both the graph and its bi-adjacency matrix where if , if . We denote the association of to by . Without loss of generality we take . We assume that each row of has nonzero entries (thus ) with the following properties on its SVD:

  • The left and right top singular vectors of are and , respectively. This implies that , where

    denotes the largest singular value of

    , and that each column of has nonzero entries.

  • We have , where denotes the second largest singular value of and is some universal constant.

Thus we require to have a large enough spectral gap. As discussed in [7], an Erdös-Renyi random graph with average degree satisfies this spectral gap property with high probability, and so do stochastic block models for certain choices of inter- and intra-cluster edge connection probabilities. Thus, this sampling scheme is more general than a uniform sampling assumption, used in [4], and it also includes the stochastic block model [7] resulting in non-uniform sampling.

Iii Performance Upperbound

We now present a performance bound for the solution to (4). With , define


where is the bound on the entry-wise infinity-norm of (see (4)). For the logit model, we have , and . For the probit model we obtain , . For further reference, define the constraint set

Theorem III.1

Suppose that , and satisfies assumptions (A1) and (A2), with . Further, suppose is generated according to (1) and is log-concave in . Then with probability at least , any global minimizer of (4) satisfies


provided . Here, are universal constants, is given by assumption (A2), and

with and given by (5), (6).

Proof of this theorem is given in Sec. VI. Of particular interest is the case where is fixed and we let and become large, with fixed. In this case we have the following Corollary.

Corollary III.2

Assume the conditions of Theorem III.1. Let be fixed independent of and . Then with probability at least , any global minimum to (4) satisfies


Iii-a Comparison with previous work

Consider , with fraction of its entries sampled, such that (also assumed in [4, 5]) and . Then , and . The bounds proposed in [4] (and [5] in case of uniform sampling) yields


whereas, applying our result (Corollary 3.2), we obtain


Comparing (11) and (12), we see our method has faster convergence rate in for fixed rank and fraction of revealed entries . Notice that if the number of missing entries scales with according to , [4] yields bounded error while our bound grows with ; in our case we need to be of order at least . We believe this to be an artifact of our proof, as our numerical results (Fig. 1) show our method outperforms [4], especially for low values of and higher values of rank .

Iv Optimization

We will solve the optimization problem (4) using a log-barrier penalty function approach [9, Sec. 11.2]. The constraint translates to the log-barrier penalty function . This leads to the regularized objective function


and the optimization problem


We can account for the rank constraint in (14) via the factorization technique of [10, 11, 12] where instead of optimizing with respect to in (4), is factorized into two matrices and such that . One then chooses and optimizes with respect to the factors . The reformulated objective function is then given by


where denotes the th row of , and the th row of . The parameter sets the accuracy of approximation of via the log-barrier function. We solve this factored version using a gradient descent method with backtracking line search, in a sequence of central path following solutions [9, Sec. 11.2], where one gradually reduces toward 0. Initial values of are randomly picked and scaled to satisfy . Starting with a large , we solve for via central path following and use 5-fold cross validation error over as the stopping criterion in selecting .

Figure 1: Relative MSE for varied values of . Probit model, rank , , , . “trace-norm” refers to [4], “max-norm” is the method of [5] for known .
Remark IV.1

The hard rank constraint results in a nonconvex constraint set. Thus, (4) and (14) are nonconvex optimization problems; similarly for minimization of (15) for which the rank constraint is implicit in the factorization of . However, the following result is shown in [10, Proposition 4], based on [11], for nonconvex problems of this form. If is a local minimum of the factorized problem, then is the global minimum of problem (14), so long as and are rank-deficient. (Rank deficiency is a sufficient condition, not necessary.) This result is utilized in [12] and [5] for problems of this form.

V Numerical Experiments

V-a Synthetic Data

In this section, we test our method on synthetic data and compare it with the methods of [4, 5]. We set and construct as where and are matrices with i.i.d. entries drawn from a uniform distribution on (as in [5, 4]). Then we scale to achieve . We pick , vary matrix sizes or 400. We generate the set of revealed indices via the Bernoulli sampling model of [4] with fraction of revealed entries. We consider the probit (, as in [5, 4]) model. For Fig. 1, we take and vary . The resulting relative mean-square error (MSE) averaged over 20 Monte Carlo runs is shown in Fig. 1. As expected, the performance improves with increasing . For comparison, we have also implemented the methods of [4, 5], labeled “trace norm”, and “max-norm” respectively. As we can see our proposed approach significantly outperforms [4, 5], especially for low values of and high values of .

Figure 2: Log-log plots of relative MSE for varied

Figure 3: Relative MSE versus under the probit model, for varied p, fixed

In Fig. 2 we show the relative MSE for , for the probit model using our approach. We also plot the line in Fig. 2 to show the scale of the upper bound established in Section III. As we can see, the empirical estimation errors follow approximately the same scaling, suggesting that our analysis is tight, up to a constant.

We additionally plot the MSE for and in Fig. 3, with varying and keeping , under the probit model. This enables us to study the performance of the model under nonuniform sampling. Note that when , the spectral gap is largest and MSE is the smallest, and as gets larger, the spectral gap decreases, leading to larger MSE.

% Accuracy (Logit Model)
% Training Proposed Trace-norm Max-norm
95 72.30.7 72.40.6 71.50.7
10 60.40.6 58.50.5 58.40.6
5 53.70.8 49.20.7 50.30.2
Table I: Accuracy of the proposed, trace-norm [4] and max-norm [5] approaches on the MovieLens 100k dataset for different amounts of training data. Accuracy represents the percentage of test set ratings for which the estimate of accurately predicts the sign, i.e., whether the unobserved ratings were above or below the average rating.

V-B MovieLens (100k) Dataset

As in [4], we consider the MovieLens (100k) dataset (http://www.grouplens.org/node/73). This dataset consists of 100,000 movie ratings from 943 users on 1682 movies, with ratings on a scale from 1 to 5. Following [4], these ratings were converted to binary observations by comparing each rating to the average rating for the entire dataset. We used three splits of the data into training/test subsets and used 20 random realizations of these splits. The performance is evaluated by checking to see if the estimate of accurately predicts the sign of the test set ratings (whether the observed ratings were above or below the average rating). As in [4], we determine the needed parameter values by performing a grid search and selecting the values that lead to the best performance; we fixed , and varied (i.e. central path following), and rank . Our performance results are shown in Table I using a logistic model for three approaches: proposed, [4, 5]. These results support our findings on synthetic data that our method is preferable over [4, 5] for sparser data.

Vi Proof of Theorem iii.1

Our proof is based on a second-order Taylor series expansion and a matrix concentration inequality.

Let and . The objective function is continuous in and the set is compact, therefore, achieves a minimum in . If minimizes subject to the constraints, then where . By the second-order Taylor’s theorem, expanding around we have


where for some , with corresponding matrix . We need several auxiliary results before we can prove Theorem III.1.

Using (3), it follows that




Let . Note that by our notation,

We then have


where . Let . Therefore,

Using (1) and (6), we have


We need the following result from [13] concerning spectral norms of random matrices for Lemma VI.2.

Lemma VI.1

[13, Theorem 8.4] Take any two numbers and such that . Suppose that

is a matrix whose entries are independent random variables that satisfy, for some


Suppose that for some . Then

where is a constant that depends only on and is a positive universal constant. The same result is true when and

is symmetric or skew-symmetric, with independent entries on and above the diagonal, all other assumptions remaining the same. Lastly, all results remain true if the assumption

is changed to .

Lemma VI.2

Let , and . Then with probability at least , we have

where , is a constant that depends only on and is a positive universal constant.

Using (20), we have


Let . Then we have , and . Applying Lemma VI.1 to with , we obtain with probability at least for some positive constants and . W note that for any matrix of rank , with denoting the nuclear norm. Hence , yielding the desired result.

Lemma VI.3

Let and . Then for any and any , we have

Using (5), (18) and (19), we have


which completes the proof.

We need a result similar to [7, Theorem 4.1] regarding closeness of a fixed matrix to its sampled version, which is proved therein for square matrices under an incoherence assumption on . In Lemma VI.4 we prove a similar result for rectangular with bounded . Define

where for a matrix , denotes the largest norm of the rows in , i.e, .

For , , and define the operator as

Lemma VI.4

Let satisfy assumptions (A1) and (A2) in Section IV. Let with rank. Then we have


By definition of , there exist and for some such that , and . Since , we have , but this fact is not needed in our proof. By the variational definition of operator norm,

We also have where denotes the Hadamard (elementwise) product. Letting and respectively denote the -th column of and , we write

We therefore have

Normalize to unit norm as , and similarly for . Let where is a unit norm vector orthogonal to . Then . Hence


where we used the facts that and . Since is the top left singular vector of , we have

Using the above inequality in (26) we obtain


We have . Hence, . Therefore,


where we used . Similarly, we have


It then follows from (27)-(29) that

This establishes (24). Now use [5] and to establish (25).

Lemma VI.5

Let . Then we have

Let , , and . Then by Lemma VI.4 and the fact that rank, we have


Using , (31) can be expressed as . Since , we then have . Since , we have