'Null space' presentation slideshows

Robust View-Invariant Representation for Classification and Retrieval of Image and Video Data

Robust View-Invariant Representation for Classification and Retrieval of Image and Video Data. Xu Chen University of Illinois at Chicago Electrical and Computer Engineering March/01/2010. Outline. Background and Motivation Related Work Problem Statement Expected Contributions

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NLP

831 views • 58 slides

Last lecture summary

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ME 4135 Robotics & Control

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Last lecture summary

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Animation CS 551 / 651

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ima.umn /2008-2009/ND6.15-26.09

MATH:7450 (22M:305) Topics in Topology: Scientific and Engineering Applications of Algebraic Topology Sept 6, 2013: Calculating homology using matrices Fall 2013 course offered through the University of Iowa Division of Continuing Education Isabel K. Darcy, Department of Mathematics

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examples: eigenvalues, eigenvectors and diagonability

examples: eigenvalues, eigenvectors and diagonability. Pamela Leutwyler. Find the eigenvalues and eigenvectors. next. next. next. next. next. next. next. next. characteristic polynomial. next. characteristic polynomial. next. potential rational roots:1,-1,3,-3,9,-9.

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Review of Linear Algebra

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link.springer /article/10.1007/s00454-002-2885-2

MATH:7450 (22M:305) Topics in Topology: Scientific and Engineering Applications of Algebraic Topology Sept 13, 2013: Persistent homology II Fall 2013 course offered through the University of Iowa Division of Continuing Education Isabel K. Darcy, Department of Mathematics

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SVD & LSI

SVD & LSI. ML Reading Group Jan-24-2006 Presenter: Zheng Zhao. SVD ( Singular value decomposition). Vector Norm Matrix Norm Singular value decomposition The application of SVD. vector norm . A vector norm has the following properties. 1. || x || ? 0 (non-negative)

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CS 395/495-26: Spring 2003

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Lecture 9 Symmetric Matrices Subspaces and Nullspaces

Lecture 9 Symmetric Matrices Subspaces and Nullspaces. Shang-Hua Teng. Matrix Transpose. Addition: A+B Multiplication: AB Inverse: A -1 Transpose : A -T. Transpose. Inner Product and Outer Product. Properties of Transpose. End of Page 109: for a transparent proof. 0. R. r.

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Image alignment

Image alignment. Image from http://graphics.cs.cmu.edu/courses/15-463/2010_fall/. A look into the past. http://blog.flickr.net/en/2010/01/27/a-look-into-the-past/. A look into the past. Leningrad during the blockade. http://komen-dant.livejournal.com/345684.html. Bing streetside images.

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Example: Given a matrix defining a linear mapping Find a basis for the null space and a basis for the range

Example: Given a matrix defining a linear mapping Find a basis for the null space and a basis for the range. Pamela Leutwyler. Let M be the matrix for the linear mapping T ( ie: ). Let M be the matrix for the linear mapping T

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click to start

click to start. S. PHONE BOOTH. S. PHONE BOOTH. S. S. S. S. S. S. S. S. S. S. PHONE BOOTH. S. TRANSFORMATION. S. Example: A LINEAR TRANSFORMATION. If ( x,y ) is a point on y = x + 2 then. Add the vector . to every vector in the null space and you get

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Section 4.2

Section 4.2. Null Spaces, Column Spaces, and Linear Transformations. In this section we will look at two important subspaces associated with a matrix, the null space and the column space . Consider the following system of homogeneous equations : . (1).

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5. Eigenvalues and Eigenvectors

5. Eigenvalues and Eigenvectors. 5.1 Eigenvectors and Eigenvalues. For. eigenvalue. eigenvector. Definition

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Motivation: The electromagnetic spectrum is running out

Blind null-space tracking for MIMO Underlay Cognitive Radios Alexandros Manolakos, Yair Noam, Konstantinos Dimou, Andrea Goldsmith Electrical Engineering, Stanford University. Introduction. The BNSL Algorithm. Channel Model. Null-Space Variations. Motivation:

128 views • 1 slides