1. Some Topics Deserved Concerns Songcan Chen 2013.3.6

2. Outlines Copula & its applications Kronecker Decomposition for Matrix Covariance Descriptors & Metric on manifold

3. [1] Fabrizio Durante and Carlo Sempi, Copula Theory: An Introduction (Chapt. 1), P. Jaworski et al. (eds.), Copula Theory and Its Applications, Lecture Notes in Statistics 198,2010. [2] Jean-David Fermanian, An overview of the goodness-of-fit test problem for copulas (Chapt 1), arXiv: 19 Nov. 2012. Applications [A1] David Lopez-Paz, Jose Miguel Hernandez-Lobato, Bernhard Scholkopf, Semi- Supervised Domain Adaptation with Non-Parametric Copulas, NIPS2012/arXiv:1 Jan,2013. [A2] David Lopez-Paz, et al, Gaussian Process Vine Copulas for Multivariate Dependence, ICML2013/arXiv: 16 Feb. 2013. [A3] Carlos Almeida, et al, Modeling high dimensional time-varying dependence using D-vine SCAR models, arXiv: 9 Feb. 2012. [A4] Alexander Baue, et al, Pair-copula Bayesian networks, arXiv:23 Nov. 2012. … … Copula & its applications

4. Kronecker Decomposition for Matrix [1] C. V. Loan and N. Pitsianis, Approximation with kronecker products, in Linear Algebra for Large Scale and Real Time Applications. Kluwer Publications, 1993, pp. 293–314. [2] T. Tsiligkaridis, A. Hero, and S. Zhou, On Convergence of Kronecker Graphical Lasso Algorithms, to appear in IEEE TSP, 2013. [3] ---, Convergence Properties of Kronecker Graphical Lasso Algorithms, arXiv:1204.0585, July 2012. [4] ---, Low Separation Rank Covariance Estimation using Kronecker Product Expansions, google 2013. [5] --- Covariance Estimation in High Dimensions via Kronecker Product Expansions, arXiv:12 Feb. 2013. [6] --- SPARSE COVARIANCE ESTIMATION UNDER KRONECKER PRODUCT STRUCTURE, ICCASP2012,pp:3633-3636. [7] Marco F. Duarte, Richard G. Baraniuk, Kronecker Compressive Sensing, IEEE TIP, 21(2)494-504 2012 [8] MARTIN SINGULL, et al, More on the Kronecker Structured Covariance Matrix, Communications in Statistics—Theory and Methods, 41: 2512–2523, 2012

5. Covariance Descriptor [1] Oncel Tuzel, Fatih Porikli, and Peter Meer,Region Covariance-A Fast Descriptor for Detection and Classification, Tech. Report 2005. [2] Yanwei Pang, Yuan Yuan, Xuelong Li, Gabor-Based Region Covariance Matrices for Face Recognition, IEEE T CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 18(7):989-993,2008 [3] Anoop Cherian, et al, Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices, IEEE TPAMI, in press, 2012. [4] Pedro Cortez Cargill,et al, Object Tracking based on Covariance Descriptors and On-Line Naive Bayes Nearest Neighbor Classifier, 2010 4th Pacific-Rim Symp. Image and Video Technology,pp.139-144. [5] Ravishankar Sivalingam, et al, Positive Definite Dictionary Learning for Region Covariances, ICCV 2011. [6] Mehrtash T. Harandi, et al, Kernel Analysis over Riemannian Manifolds for Visual Recognition of Actions, Pedestrians and Textures, CVPR2012.

6. Copula & its applications

7. What is Copula? Definition Copulas are statistical tools that factorize multivariate distributions into the product of its marginals and a function that captures any possible form of dependence among them (marginals). This function is referred to as the copula, and it links the marginals together into the joint multivariate model.

8. What is Copula? Mathematical formulation: P(x i ) is the marginal cdf of the random variable x i. Interestingly, this density has uniform marginals, since P(z)~ U[0; 1] for any random variable z. When P(x 1 ); … ; P(x d ) are continuous, the copula c(.) is unique (2)

9. Especially, when factorizing multivariate densities into a product of marginal distributions and bivariate copula functions (called as vines). Each of these factors corresponds to one of the building blocks that are assumed either constant or varying across different learning domains.  applicable to DA, TL and MTL!

10. Characteristics Infinitely many multivariate models share the same underlying copula function!

11. main advantage allowed to model separately the marginal distributions and the dependencies linking them together to produce the multivariate model subject of study.

12. Estimate p(x) from given samples Step 1: Construct estimates of the marginal pdfs  cdfs Step 2: Combine them

13. Estimate marginal pdfs and cdfs Parametric (copula) manners Examples: Gaussian, Gumbel, Frank, Clayton or Student copulas, etc. Weaknesses: Real-world data often exhibit complex dependencies which cannot be correctly described!

14. Illustration of Weaknesses

15. Non-parametric manners Using unidimensional KDEs. Illustration of estimation for Bivariate Copulas Estimate marginal pdfs and cdfs

16. Non-parametric Bivariate Copulas Estimating: Now From pdf to cdf (pseudo-sample from its copula c): Where r.v. (u, v): (4)

17. Non-parametric Bivariate Copulas (u,v)’s joint density is the copula function c(u; v)! Using KDE with Gaussian kernels can approximate c(u; v)! but will lead to (u,v)’s support of [0,1]x[0,1] rather than R 2 ! Instead, performing the density estimation in a transformed space: Selecting some continuous distribution with support on R, strictly positive density, cumulative distribution and quantile function. Let their joint pdf: (6)

18. Non-parametric Bivariate Copulas The copula of this new density is identical to the copula of (4), since the performed transformations are marginal- wise and the support of (6) is now R 2 ; Specially using Gauss density, having See [A1] for more details of derivation!

19. Non-parametric Multivariate Copulas From Bivariate (pair copula) to multivariate (copula): Extension Trick: Introduction of R-vine

20. Domain Adaptation: Non-linear regression with continuous data regression Given the source pdf: And solving a target task with density:

21. DA of Non-linear regression Given the data available for both tasks, our objective is to build a good estimate for the conditional density To address this domain adaptation problem, we assume that p t is a modified version of p s, In particular, we assume that p t is obtained in two steps from p s.

22. DA of Non-linear regression Step1: p s is expressed using an R-vine representation as follows: Step2: Some of the factors included in that representation (marginal distributions or pairwise copulas) are modified to derive p t. All we need to address the adaptation across domains is to reconstruct the R-vine representation of p s using data from the source task, and then identify which of the factors have been modified to produce p t. These factors are corrected using data from the target task.

23. DA of Non-linear regression A Key : Changes in these factors across different domains can be detected using two sample tests (such as MMD), and transferred across domains in order to adapt the target task density model! See [A1] for more details! Maximum Mean Discrepancy (MMD) will return low p-values when two samples are unlikely to have been drawn from the same distribution!

24. Insights How to extend the copula with image patches? How to apply it to multiview learning with (semi-) pairing or/and (semi-)supervision? How to adapt the universum to such new problem? How to apply it to zero-data learning? Tailor it to 2D (even Tensor) copula …

25. Kronecker Product Decomposition for (Covariance) Matrix

26. Kronecker Product (KP) Covariance [1] C. V. Loan and N. Pitsianis, Approximation with kronecker products, in Linear Algebra for Large Scale and Real Time Applications. Kluwer Publications, 1993, pp. 293–314. [1] proves that any pqxpq matrix ∑ 0 can be written as an orthogonal expansion of KPs of the form (1), thus allowing any covariance matrix to be arbitrarily approximated by a bilinear decomposition of the form (1). (1)

27. Estimation of HD Covariance matrix Applications Channel modeling for MIMO wireless communications, Geo-statistics, Genomics, Multi-task learning, Face recognition, Recommendation systems, Collaborative filtering, …

28. Estimation of HD Covariance matrix Main difficulty of estimation via the maximum likelihood principle: The nonconvexity of optimization problem! Seeking alternatives! 1) The flip flop (FF) algorithm [WJS08]; 2) Penalized Least squares (PLS)[Lou12] 3) PERMUTED RANK-PLS (PRLS)[5] [WJS08] K. Werner, M. Jansson, and P. Stoica, On estimation of covariance matrices with Kronecker product structure, IEEE TSP, 56(2), 2008. [Lou12]K. Lounici, “High-dimensional covariance matrix estimation with missing observations,” arXiv:1201.2577v5, May 2012

29. PLS Sample covariance matrix (SCM): with 0 means and covariance (1) (2) (3)

30. PRLS (4) (5) As a result, the closed-form solution of (4) is

31. A Theorem See [5] for more details!

32. Other estimation for KP structured covariance estimation

33. The basic Kronecker model is The ML objective:

34. Use The problem (58) turns to

35. Hybrid Robust Kronecker Model The ML objective: Solving for Σ>0 again via Lemma 4 yields

36. the problem (73) reduces to Solve (75) using the fixed point iteration Arbitrary can be used as initial iteration.

37. … Insights (1)

38. 1) Metric Learning (ML) ML&CL, Relative Distance constraints, LMNN-like,… 2) Classification learning Predictive function: f(X)=tr(W T X)+b; The objective: Insights (2)

39. ML across heterogeneous domains 2 lines: 1) Line 1: 2) Line 2 (for ML&CL) Symmetry and PSD An indefinite measure ({U i } is base & { α i } is sparsified) Implying that 2 lines can be unified to a common indefinite ML!

40. Noise model Where c is the c-th class or cluster, e ci is noise and o ci is outlier and its ||o ci ||≠0 if outlier, 0 otherwise. Discuss: 1)U c =0, o ci =0; e ci ~N(0, dI)  Means; Lap(0,dI)  Medians; other priors  other statistics 2)U c ≠ 0, o ci =0; e ci ~ N(0, dI)  PCA; Lap(0,dI)  L 1 -PCA; other priors  other PCAs; Insights (4)

41. 3) U c =0, o ci ≠0; e ci ~N(0, dI)  Robust (k-)Means; ~ Lap(0,dI)  (k-)Medians; 4) Subspace U c ≠0, o ci ≠0; e ci ~N(0, dI)  Robust k-subspaces; 5) m c =0 …… 6) Robust (Semi-)NMF …… 7) Robust CA …… where noise model:Γ=BA T Υ+E+O

42. Covariance Descriptor (CD)

43. Applications of CD Multi-camera object tracking; Human detection, Hmage segmentation, Texture segmentation, Robust face recognition, Emotion recognition, Human action recognition, Speech recognition … [3] Anoop Cherian, et al, Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices, IEEE TPAMI, in press, 2012.

44. CD for Image and vision I: an intensity or color image. F: WxHxd feature image extracted from I by (1) where the function can be any mapping such as intensity, color, gradients, filter responses, etc. E.g.,

45. CD for Image and vision For a given rectangular region R in F, let {z k }, k=1..n be the d-dimensional feature points inside R, the CD of R is defined (2)

46. CD for Face Image Object representation: Construct five covariance matrices from overlapping regions of an object feature image. The covariances are used as the object descriptors!

47. CD for Textures Texture representation. There are u images for each texture class and we sample s regions from each image and compute covariance matrices C

48. Advantages A single covariance matrix extracted from a region is usually enough to match the region in different views and poses; a natural way of fusing multiple features which might be correlated; low-dimensional compared to other region descriptors and due to symmetry C R ; a certain scale and rotation invariance over the regions in different images due to regardless of the ordering and the number of points. Fast in calculation via integral image!

49. Matching Key: Distance Measures between SPD matrices! Known: All SPD matrices with the size form a Riemannian manifold! Thus the distance between 2 SPDs can be measured using geodesics! However, computing similarity between covariance matrices is non-trivial.

50. Metrics between 2 SPD Matrices X and Y Affine Invariant Riemannian Metric (AIRM) Log-Euclidean Riemannian Metric (LERM)

51. Metrics between 2 SPD Matrices X and Y Symmetrized KL-Divergence Metric (KLDM) Jensen-Bregman LogDet Divergence (JBLD)

52. Properties of JBLD

53. Important Theorems (1)

54. Important Theorems (2)

55. Computing time (1)

56. Computing time (2)

57. K-means with JBLD Objective

58. Isosurface plots for various distance measures (a) Frobenius distance, (b) AIRM, (c) KLDM, and (d) JBLD

59. Table 3, A comparison of various metrics on covariances and their computational complexities against JBLD See [3] for more details! [3] Anoop Cherian, et al, Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices, IEEE TPAMI, in press, 2012.

60. Insights How to extend CD to text? Key: define CD on general graph with discrete operators on graph, including local: derivative, gradient, difference, etc.. global: centrality, etc.. Tailor CD to 2D classifier with various scenarios KP and PDF defined on CD Copula on CD! Extend it to multiview with heterogeneous sources! …

61. Thanks! Q&A