1. Bayesian Robust Principal Component Analysis Presenter: Raghu Ranganathan ECE / CMR Tennessee Technological University January 21, 2011 Reading Group (Xinghao Ding, Lihan He, and Lawrence Carin)

2. Paper contribution ■The problem of matrix decomposition into low-rank and sparse components is considered employing a hierarchical approach ■The matrix is assumed noisy, with unknown and possibly non- stationary noise statistics ■The Bayesian framework approximately infers the noise statistics in addition to the low-rank and sparse outlier contributions ■The model proposed is robust to a broad range of noise levels without having to change the hyper-parameter settings ■In addition, a Markov dependency between successive rows of the matrix is inferred by the Bayesian model to exploit additional structure in the observed matrix, particularly, in video applications 21/21/11

3. Introduction ■Most high-dimensional data such as images, biological data, and social network data (Netflix data) reside in a low- dimensional subspace or low-dimensional manifold 31/21/11

4. Noise models ■In low-rank matrix representations, two types of noise models are usually considered ■One causes small scale perturbation to all the matrix elements, e.g. i.i.d. Gaussian noise added to each element. ■In this case, if the noise energy is small compared to the dominant singular values of the SVD, it does not significantly affect the principal vectors ■The second case is sparse noise with arbitrary magnitude, impacting a small subset of matrix elements, for example a moving object in video, in the presence of a static background manifests such sparse noise 41/21/11

5. Convex optimization approach 511/5/10

6. Bayesian approach ■The observation matrix is considered to be of the form Y = L (low-rank)+ S (sparse)+ E (noise), with the presence of both sparse noise, S, and dense noise E. ■In the proposed Bayesian model, the noise statistics of E are approximately learned, along with learning S, and L. ■The proposed model is robust to a broad range of noise variances ■The Bayesian model infers approximation to the posterior distributions on the model parameters, and obtains approximate probability distributions for L, S, and E ■The advantage of Bayesian model is that prior knowledge is employed in the inference 61/21/11

7. Bayesian approach ■The Bayesian framework exploits the anticipated structure in the sparse component. ■In video analysis, it is desired to separate the spatially localized moving objects (sparse component), from the static or quasi-static background (low-rank component) in the presence frame dependent additive noise E. ■The correlation between the sparse components of the video from frame to frame (column to column in the matrix) has to be considered ■In this paper, a Markov dependency in time and space is assumed between the sparse components of consecutive matrix columns ■This structure is incorporated into the Bayesian framework, with the Markov parameters inferred through the observed matrix 71/21/11

8. Bayesian Robust PCA ■The work in this paper is closely related to the low-rank matrix completion problem where we try to approximate a matrix (with noisy entries) by a low- rank matrix and to predict the missing entries ■The matrix Y = L + S + E is missing random entries; the proposed model can make estimates for the missing entries (in terms of the low-rank term L) ■The S term is defined as a sparse set of matrix entries; the location of S must be inferred while estimating the values of L, S, and E ■Typically, in Bayesian inference, a sparseness promoting prior is imposed on the desired signal, and the posterior distribution of the sparse signal is inferred. 81/21/11

9. Bayesian Low-rank and Sparse Model 91/21/11

10. 1011/5/10

11. Bayesian Low-rank and Sparse Model 1111/5/10

12. Bayesian Low-rank and Sparse Model 1211/5/10

13. C. Noise component ■The measurement noise is drawn i.i.d from a Gaussian distribution, and the noise affects all measurements ■The noise variance is assumed unknown, and is learned within the model inference. Mathematically, the noise is modeled as ■The model can learn different noise variances for different parts of E, i.e. each column/row of Y (each frame) in general have its own noise level. The noise structure is modified as 131/21/11

14. Relation to the optimization based approach 141/21/11

15. Relation to the optimization based approach ■In the Bayesian model, it is not required to know the noise variance a priori, the model will learn the noise during inference ■For the low-rank component instead of the constraint to impose sparseness of singular values, the Gaussian prior together with the beta- Bernoulli distribution is used to obtain an constraint ■For the sparse component, instead of the constraint, the constraint and the beta-Bernoulli distribution is employed to enforce sparsity ■Compared to the Laplacian prior (gives many small entries close to 0), the beta-Bernoulli prior yields exactly zero values ■In Bayesian learning, numerical methods are used to estimate the distribution for the unknown parameters, whereas in the optimization based approach, a solution to the minimum of a function similar to 151/21/11

16. Markov dependency of Sparse Term in Time and Space 161/21/11

17. Markov dependency of Sparse Term in Time and Space 171/21/11

18. Markov dependency of Sparse Term in Time and Space 181/21/11

19. Posterior inference 191/21/11

20. 2011/5/10

21. Experimental results 2111/5/10

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24. B. Video example ■The application of video surveillance with a fixed camera is considered ■The objective is to reconstruct a near static background and moving foreground from a video sequence ■The data are organized such that the column m of Y is constructed by concatenating all pixels of frame m from a grayscale video sequence ■The background is modeled as the low-rank component, and the moving foreground as the sparse component. ■The rank r is usually small for a static background, and the sparse components across frames (columns of Y) are strongly correlated, modeled by a Markov dependency 241/21/11

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27. Conclusions ■The authors have developed a new robust Bayesian PCA framework for analysis of matrices with sparsely distributed noise of arbitrary magnitude ■The Bayesian approach is found to be robust to densely distributed noise, and the noise statistics may be inferred based on the data, with no tuning of hyperparameters ■In addition, using the Markov property, the model allows the noise statistics to vary from frame to frame ■Future research directions would involve a moving camera which would assume the background resides in a low-dimensional manifold as opposed to low-dimensional linear subspace ■The Bayesian framework may be extended to infer the properties of the low- dimensional manifold 271/21/11