PPT-Maximum Covariance Analysis

Author : giovanna-bartolotta | Published Date : 2016-03-11

Canonical Correlation Analysis FIG 6 The CCA mode 1 for a SLP and b SST The pattern in a is scaled by maxu min u2 and b by max v min v 2

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Maximum Covariance Analysis: Transcript

Canonical Correlation Analysis FIG 6 The CCA mode 1 for a SLP and b SST The pattern in a is scaled by maxu min u2 and b by max v min v 2. Lecture . 8. Data Processing and Representation. Principal Component Analysis (PCA). G53MLE Machine Learning Dr Guoping Qiu. 1. Problems. Object Detection. 2. G53MLE Machine Learning Dr Guoping Qiu. Problems. Orbit . Determination . I. Fall . 2014. Professor Brandon A. . Jones. Lecture 13: Probability and Statistics (Part 3). Lecture Quiz . and Homework Due Friday. 2. Announcements. 3. Lecture Quiz Results. R. F. . Riesenfeld. (. Based on web slides by . James H. . Steiger. ). Goals. Introduce concepts of . Covariance. Correlation. Develop computational formulas. 2. R F Riesenfeld Sp 2010. CS5961 Comp Stat. Detection: . introduction. Approaches. Holistic detection: use local search window that meets . criterias. Part-based detection: pedestrian as a collection of parts (to be found!). Patch-based detection: local features matched against a (learned) codebook, then voting for final detection. 1) Basics. 2) Means and Values (Ch 7): summary. 3. ) Variance (Ch 8): summary. 4) Resemblance between relatives. 5) Homework (8.3). Values & means: summary. (Falconer & Mackay: chapter 7). Sanja Franic. EnKF. , EKF SLAM, Fast SLAM, Graph SLAM. Pieter . Abbeel. UC Berkeley EECS. Many . slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . PPI and SEM. Methods for Dummies . 2011/12. Emma Jayne . Kilford. & Peter . Smittenaar. History:. Functional. . Specialisation. Different areas of the brain are specialised for different functions. Naval Research Laboratory, Monterey . JCSDA Summer Colloquium. July 2012. Santa Fe, NM. Background Error Covariance Modeling. 1. Overview. Strategies for flow dependent error covariance modeling. Ensemble . (Slides borrowed from various presentations). Image representations. Templates. Intensity, gradients, etc.. Histograms. Color, texture, SIFT descriptors, etc.. Space Shuttle Cargo Bay. Image Representations: Histograms. Miriam Huntley. SEAS, Harvard University. May 15, 2013. 18.338 Course Project. RMT. Real World Data. “When it comes to RMT in the real world, we know close to nothing.”. -Prof. Alan . Edelman. , last week. Bamshad Mobasher. DePaul University. Principal Component Analysis. PCA is a widely used data . compression and dimensionality reduction technique. PCA takes a data matrix, . A. , of . n. objects by . Combines linear regression and ANOVA. Can be used to compare . g. treatments, after controlling for quantitative factor believed to be related to response (e.g. pre-treatment score). Can be used to compare regression equations among . 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. Dorothea Wiarda. William J. Marshall. Vladimir . Sobes. Friederike. . Bostelmann. Andrew Holcomb. Bradley T. Rearden. Outline. Covariance library creation. Differences between ENDF/B-VII.1 and ENDF/B-VIII.0.

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