Some Topics deserved Concerns Songcan Chen 2013.3.6
Some Topics Deserved Concerns Songcan Chen 2013.3.6
Outlines Copula its applications Kronecker decomposition for matrix Covariance Descriptors Metric on manifold
Outlines • Copula & its applications • Kronecker Decomposition for Matrix • Covariance Descriptors & Metric on manifold
Copula its applications [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 [Al] 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
[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, SemiSupervised 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
Kronecker Decomposition for matrix []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, aXv:12040585,July2012 [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 TIE,21(24945042012 8]MARTIN SINGULL, et al, More on the Kronecker Structured Covariance Matrix, Communications in Statistics-Theory and Methods, 41: 2512-2523 2012
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
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)9899932008 [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
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