1. 1 Sparsity Control for Robust Principal Component Analysis Gonzalo Mateos and Georgios B. Giannakis ECE Department, University of Minnesota Acknowledgments: NSF grants no. CCF-1016605, EECS-1002180 Asilomar Conference November 10, 2010

2. 2 2 Principal Component Analysis Our goal: robustify PCA by controlling outlier sparsity Motivation: (statistical) learning from high-dimensional data Principal component analysis (PCA) [Pearson ’ 1901]  Extraction of low-dimensional data structure  Data compression and reconstruction  PCA is non-robust to outliers [Jolliffe ’ 86] DNA microarray Traffic surveillance

3. 3 3 Our work in context Robust PCA  Robust covariance matrix estimators [Campbell ’ 80], [Huber ’ 81]  Computer vision [Xu-Yuille ’ 95], [De la Torre-Black ’ 03]  Low-rank matrix recovery from sparse errors [Wright et al ’ 09] Huber ’ s M-class and sparsity in linear regression [ Fuchs ’ 99] Contemporary applications  Anomaly detection in IP networks [Huang et al ’ 07], [Kim et al ’ 09]  Video surveillance, e.g., [Oliver et al ’ 99] OriginalRobust PCA `Outliers ’

4. 4 4 PCA formulations Training data: Minimum reconstruction error: Dimensionality reduction operator Reconstruction operator Maximum variance: Factor analysis model: Solution:

5. 5 5 Robustifying PCA Least-trimmed squares (LTS) regression [Rousseeuw ’ 87] (LTS PCA) LTS-based PCA for robustness is the -th order statistic among Trimming constant determines breakdown point Q: How should we go about minimizing ? (LTS PCA) is nonconvex; existence of minimizer(s)? A : Try all subsets of size, solve, and pick the best Simple but intractable beyond small problems

6. 6 6 Modeling outliers Remarks  and are unknown  If outliers sporadic, then vector is sparse! Introduce auxiliary variables s.t. inlier outlier  Inliers obey ; outliers something else  Inlier noise: are zero-mean i.i.d. random vectors Natural (but intractable) estimator

7. 7 7 LTS PCA as sparse regression Lagrangian form  Tuning controls sparsity in, thus number of outliers (P0) J ustifies the model and its estimator (P0); ties sparsity with robustness Proposition 1: If solves (P0) with chosen such that, then solves (LTS PCA) too.

8. 8 Just relax! (P0) is NP-hard relax (P2) Q: Does (P2) yield robust estimates ? A: Yap! Huber estimator is a special case  Role of sparsity controlling is central

9. 9 Entrywise outliers Use -norm regularization (P1) OriginalRobust PCA (P2)Robust PCA (P1) Outlier pixels Entire image rejected Outlier pixels rejected

10. 10 Alternating minimization (P1)  update: reduced-rank Procrustes rotation  update: coordinatewise soft-thresholding Proposition 2: Alg. 1 ’ s iterates converge to a stationary point of (P1).

11. 11 Refinements Nonconvex penalty terms approximate better in (P0) Options: SCAD [Fan-Li ’ 01], or sum-of-logs [Candes etal ’ 08] Iterative linearization-minimization of around Iteratively reweighted version of Alg. 1 Warm start: solution of (P1) or (P2) Bias reduction in (cf. weighted Lasso [Zou ’ 06]) Discard outliers identified in Re-estimate missing data problem

12. 12 Online robust PCA Motivation: Real-time data and memory limitations Exponentially-weighted robust PCA Approximation [Yang ’ 95] At time, do not re-estimate past outlier vectors

13. 13 Video surveillance OriginalPCARobust PCA `Outliers ’ Data: http://www.cs.cmu.edu/~ftorre/

14. 14 Online PCA in action Angle between C(n) and C Inliers: Outliers: Figure of merit: angle between and

15. 15 Concluding summary Sparsity control for robust PCA  LTS PCA as -(pseudo)norm regularized regression (NP-hard)  Relaxation (group)-Lassoed PCA M-type estimator  Sparsity controlling role of central Tests on real video surveillance data for anomaly extraction Batch and online robust PCA algorithms i) Outlier identification, ii) Robust subspace tracking Refinements via nonconvex penalty terms Ongoing research Preference measurement: conjoint analysis and collaborative filtering Robustifying kernel PCA and blind dictionary learning