PPT-Sparse Inverse Covariance Estimation with Graphical LASSO

Author : danika-pritchard | Published Date : 2016-05-03

J Friedman T Hastie R Tibshirani Biostatistics 2008 Presented by Minhua Chen 1 Motivation Mathematical Model Mathematical Tools Graphical LASSO Related papers

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Sparse Inverse Covariance Estimation with Graphical LASSO: Transcript

J Friedman T Hastie R Tibshirani Biostatistics 2008 Presented by Minhua Chen 1 Motivation Mathematical Model Mathematical Tools Graphical LASSO Related papers 2 Outline Motivation. Compressive Sensing of Videos. Venue. CVPR 2012, Providence, RI, USA. June 16, 2012. Organizers. :. Richard G. . Baraniuk. Mohit. Gupta. Aswin C. Sankaranarayanan. Ashok Veeraraghavan. Part 2: Compressive sensing. Lecture 11. Prof. Thomas Herring. Room 54-820A; 253-5941. tah@mit.edu. http://geoweb.mit.edu/~tah/12.540. . 03/13/2013. 12.540 Lec 11. 2. Statistical approach to estimation. Summary. Look at estimation from statistical point of view. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. nn Gonzalo . Mateos. , Juan A. Bazerque, and . Georgios. B. Giannakis . Acknowledgement:. . NSF grants . CCF-0830480, 1016605 and . ECCS-0824007. January 6, 2011. Distributed sparse estimation. 2. Data acquired by . onto convex sets. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . joint work with . Stephen Becker. Anastasios. . Kyrillidis. ISMP’12. Generalized covariance matrices and their inverses. Menglong Li. Ph.d. of Industrial Engineering. Dec 1. st. 2016. Outline. Recap: Gaussian graphical model. Extend to general graphical model. Model setting. to Multiple Correspondence . Analysis. G. Saporta. 1. , . A. . . Bernard. 1,2. , . C. . . Guinot. 2,3. 1 . CNAM, Paris, France. 2 . CE.R.I.E.S., Neuilly sur Seine, France. 3 . Université. . François Rabelais. 1. . To develop methods for determining effects of acceleration noise and orbit selection on geopotential estimation errors for Low-Low Satellite-to-Satellite Tracking mission.. 2. Compare the statistical covariance of geopotential estimates to actual estimation error, so that the statistical error can be used in mission design, which is far less computationally intensive compared to a full non-linear estimation process.. J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. Gareth R. Barnes. Format. What is an inverse problem. Prior knowledge- links to popular algorithms.. Validation of prior knowledge/ Model evidence. Inverse problems aren’t difficult. The . forward. Determinants. Square matrices have determinants, which are useful in other matrix operations, especially inversion. .. For a second-order . square. . matrix. , . A. ,. the determinant is. Consider the following bivariate raw data matrix. An introduction. CS578-Digital speech signal processing. Invited lecture. On the (Glottal) Inverse Filtering of Speech Signals. Introduction. Inverse Filtering Techniques. Conclusions. Introduction. On the (Glottal) Inverse Filtering of Speech Signals. Gareth R. Barnes. Format. The inverse problem. Choice of prior knowledge in some popular algorithms. Why the solution is important.. Volume currents. Magnetic field. Electrical potential difference (EEG).

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