Rutgers, The State University of New Jersey Iterative Embedding with Robust Correction using Feedback of Error Observed Praneeth Vepakomma 1 Ahmed Elgammal.

Slides:



Advertisements
Similar presentations
Department of Computer Science and Engineering Normal Estimation for Point Clouds: A Comparison Study for a Voronoi Based Method Tamal K. DeyGang LiJian.
Advertisements

1 Regression as Moment Structure. 2 Regression Equation Y =  X + v Observable Variables Y z = X Moment matrix  YY  YX  =  YX  XX Moment structure.
CBS-SSB STATISTICS NETHERLANDS – STATISTICS NORWAY Work Session on Statistical Data Editing Ljubljana, Slovenia, 9-11 May 2011 Jeroen Pannekoek and Li-Chun.
Hyper Least Squares and its applications Dr.Kenichi Kanatani Dr. Hirotaka Nitsuma Dr. Yasuyuki SugayaPrasanna Rangarajan
Inexact SQP Methods for Equality Constrained Optimization Frank Edward Curtis Department of IE/MS, Northwestern University with Richard Byrd and Jorge.
SIGMETRICS 2008: Introduction to Control Theory. Abdelzaher, Diao, Hellerstein, Lu, and Zhu. CPU Utilization Control in Distributed Real-Time Systems Chenyang.
Transformation Methods MOM (Method of Multipliers) Study of Engineering Optimization Guanyao Huang RUbiNet - Robust and Ubiquitous Networking Research.
Logistic Regression Rong Jin. Logistic Regression Model  In Gaussian generative model:  Generalize the ratio to a linear model Parameters: w and c.
NORM BASED APPROACHES FOR AUTOMATIC TUNING OF MODEL BASED PREDICTIVE CONTROL Pastora Vega, Mario Francisco, Eladio Sanz University of Salamanca – Spain.
Classification with reject option in gene expression data Blaise Hanczar and Edward R Dougherty BIOINFORMATICS Vol. 24 no , pages
U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Electrical & Computer Engineering Web Enabled Patron Queuing System Professor Weibo Gong Raj Wadwal.
Data Modeling and Least Squares Fitting 2 COS 323.
Efficient Simulation of Physical System Models Using Inlined Implicit Runge-Kutta Algorithms Vicha Treeaporn Department of Electrical & Computer Engineering.
Branch and Bound Algorithm for Solving Integer Linear Programming
Energy-efficient Self-adapting Online Linear Forecasting for Wireless Sensor Network Applications Jai-Jin Lim and Kang G. Shin Real-Time Computing Laboratory,
Bootstrapping a Heteroscedastic Regression Model with Application to 3D Rigid Motion Evaluation Bogdan Matei Peter Meer Electrical and Computer Engineering.
Principles of the Global Positioning System Lecture 10 Prof. Thomas Herring Room A;
1 Formation et Analyse d’Images Session 7 Daniela Hall 7 November 2005.
Query Optimization Allison Griffin. Importance of Optimization Time is money Queries are faster Helps everyone who uses the server Solution to speed lies.
Engineering Design Process
18th Inter-Institute Seminar, September 2011, Budapest, Hungary 1 J. Lógó, D. B. Merczel and L. Nagy Department of Structural Mechanics Budapest.
The BioAnalytics Group LLC Global Optimization Toolkit Project First Prototype Delivery.
Baoxian Zhao Hakan Aydin Dakai Zhu Computer Science Department Computer Science Department George Mason University University of Texas at San Antonio DAC.
Solving Hard Instances of FPGA Routing with a Congestion-Optimal Restrained-Norm Path Search Space Keith So School of Computer Science and Engineering.
Trust-Aware Optimal Crowdsourcing With Budget Constraint Xiangyang Liu 1, He He 2, and John S. Baras 1 1 Institute for Systems Research and Department.
University of Southern California Department Computer Science Bayesian Logistic Regression Model (Final Report) Graduate Student Teawon Han Professor Schweighofer,
Combined Central and Subspace Clustering for Computer Vision Applications Le Lu 1 René Vidal 2 1 Computer Science Department, Johns Hopkins University,
Rake’s Progress Revisited Nada Ganesh and Fritz Scheuren WSS September 24, 2014.
Reinforcement Learning Control with Robust Stability Chuck Anderson, Matt Kretchmar, Department of Computer Science, Peter Young, Department of Electrical.
TEMPLATE DESIGN © Observer Based Control of Decentralized Networked Control Systems Ahmed M. Elmahdi, Ahmad F. Taha School.
Design, Optimization, and Control for Multiscale Systems
1 Comparative Survey on Fundamental Matrix Estimation Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona.
CSE4334/5334 DATA MINING CSE4334/5334 Data Mining, Fall 2014 Department of Computer Science and Engineering, University of Texas at Arlington Chengkai.
Introduction to Scientific Computing II Multigrid Dr. Miriam Mehl Institut für Informatik Scientific Computing In Computer Science.
Bundle Adjustment A Modern Synthesis Bill Triggs, Philip McLauchlan, Richard Hartley and Andrew Fitzgibbon Presentation by Marios Xanthidis 5 th of No.
Inexact SQP methods for equality constrained optimization Frank Edward Curtis Department of IE/MS, Northwestern University with Richard Byrd and Jorge.
New inclusion functions in interval global optimization of engineering structures Andrzej Pownuk Chair of Theoretical Mechanics Faculty of Civil Engineering.
Optimization in Engineering Design 1 Introduction to Non-Linear Optimization.
1 On the Channel Capacity of Wireless Fading Channels C. D. Charalambous and S. Z. Denic School of Information Technology and Engineering, University of.
1 WRF-EnKF Lightning Assimilation Real-Observation Experiments Overview Cliff Mass, Greg Hakim, Phil Regulski Department of Atmospheric Sciences University.
Statistical Data Analysis 2011/2012 M. de Gunst Lecture 5.
Instructional Design Document Simplex Method - Optimization STAM Interactive Solutions.
National Taiwan University Department of Computer Science and Information Engineering An Approximation Algorithm for Haplotype Inference by Maximum Parsimony.
OPTIMAL PLANNING OF ENERGY EFFICIENCY PROGRAMS WITH THE PAID-FROM-SAVINGS STRATEGY Seyedmohammadhossein Hosseinian Ph.D. Student, Zachry Department of.
A generic procedure for simultaneous estimation of monotone trends and seasonal patterns in time series of environmental data by Mohamed Hussian and Anders.
BIG DATA Initiative SMART SubstationBig Data Solution.
Root Finding Methods Fish 559; Lecture 15 a.
Adjustment of Trilateration
Managerial Economics Linear Programming
Zeyu You, Raviv Raich, Yonghong Huang (presenter)
Multiplicative updates for L1-regularized regression
ROBUST SUBSPACE LEARNING FOR VISION AND GRAPHICS
Coordination with Linear Equations
Recursive Identification of Switched ARX Hybrid Models: Exponential Convergence and Persistence of Excitation René Vidal National ICT Australia Brian D.O.Anderson.
Universal System Model of Technology
ENGG 1801 Engineering Computing
مناهــــج البحث العلمي
Engineering design is the process of devising a system, component, or process to meet desired needs. It is a decision-making process in which the basic.
Instructor :Dr. Aamer Iqbal Bhatti
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Introduction to Scientific Computing II
Department of Computer Science & Engineering, HITEC University, Taxila
Image Registration 박성진.
دانشگاه صنعتي اميركبير
Consider Covariance Analysis Example 6.9, Spring-Mass
Visual servoing: a global path-planning approach
Introduction to Scientific Computing II
Realizing Closed-loop, Online Tuning and Control for Configurable-Cache Embedded Systems: Progress and Challenges Islam S. Badreldin*, Ann Gordon-Ross*,
Presentation transcript:

Rutgers, The State University of New Jersey Iterative Embedding with Robust Correction using Feedback of Error Observed Praneeth Vepakomma 1 Ahmed Elgammal 2 Department of Statistics, Rutgers 1, Department of Computer Science, Rutgers 2 Electrical & Computer Engineering, FIU 1 Smart Public Safety Solutions, Motorola Solutions 1

Optional Presentation Title 2 Introduction Iterative Manifold Learning Interleaving Iterative Embedding & Feedback from Error Damping Effect of Outliers M-Estimation

Optional Presentation Title 3 Interleaved Approach: Manifold Learning M- Estimation At Iteration t:

Optional Presentation Title 4 Feedback Loop With Adjustment of Weights: Big Picture

Optional Presentation Title 5 Majorization Minimization: Uses a surrogate objective The surrogate bounds the original objective Exception: Touches the original objective at only one point Incremental Optimization (Monotonic Convergence)

Optional Presentation Title 6 Proposed Majorization Function: Linear Constraint:

Optional Presentation Title 7 MM Routine:

Optional Presentation Title 8 Robust Multiplicative Updates: M-Estimation with Geman Mcclure Function:

Optional Presentation Title 9 We Show Existence of Sharpest Majorizer: Implicit Positivity Constraints Majorization Function Condition:

Optional Presentation Title 10

Optional Presentation Title 11

Optional Presentation Title 12

Optional Presentation Title 13

Optional Presentation Title 14