Download presentation

Published byGeorge Oats Modified over 3 years ago

1
**Eigen Decomposition and Singular Value Decomposition**

Mani Thomas CISC 489/689

2
**Introduction Eigenvalue decomposition**

Spectral decomposition theorem Physical interpretation of eigenvalue/eigenvectors Singular Value Decomposition Importance of SVD Matrix inversion Solution to linear system of equations Solution to a homogeneous system of equations SVD application

3
**A(x) = (Ax) = (x) = (x)**

What are eigenvalues? Given a matrix, A, x is the eigenvector and is the corresponding eigenvalue if Ax = x A must be square the determinant of A - I must be equal to zero Ax - x = 0 ! x(A - I) = 0 Trivial solution is if x = 0 The non trivial solution occurs when det(A - I) = 0 Are eigenvectors are unique? If x is an eigenvector, then x is also an eigenvector and is an eigenvalue A(x) = (Ax) = (x) = (x)

4
**Calculating the Eigenvectors/values**

Expand the det(A - I) = 0 for a 2 £ 2 matrix For a 2 £ 2 matrix, this is a simple quadratic equation with two solutions (maybe complex) This “characteristic equation” can be used to solve for x

5
**Eigenvalue example Consider,**

The corresponding eigenvectors can be computed as For = 0, one possible solution is x = (2, -1) For = 5, one possible solution is x = (1, 2) For more information: Demos in Linear algebra by G. Strang,

6
**Physical interpretation**

Consider a correlation matrix, A Error ellipse with the major axis as the larger eigenvalue and the minor axis as the smaller eigenvalue

7
**Physical interpretation**

Original Variable A Original Variable B PC 1 PC 2 Orthogonal directions of greatest variance in data Projections along PC1 (Principal Component) discriminate the data most along any one axis

8
**Physical interpretation**

First principal component is the direction of greatest variability (covariance) in the data Second is the next orthogonal (uncorrelated) direction of greatest variability So first remove all the variability along the first component, and then find the next direction of greatest variability And so on … Thus each eigenvectors provides the directions of data variances in decreasing order of eigenvalues For more information: See Gram-Schmidt Orthogonalization in G. Strang’s lectures

9
**Spectral Decomposition theorem**

If A is a symmetric and positive definite k £ k matrix (xTAx > 0) with i (i > 0) and ei, i = 1 k being the k eigenvector and eigenvalue pairs, then This is also called the eigen decomposition theorem Any symmetric matrix can be reconstructed using its eigenvalues and eigenvectors Any similarity to what has been discussed before?

10
**Example for spectral decomposition**

Let A be a symmetric, positive definite matrix The eigenvectors for the corresponding eigenvalues are Consequently,

11
**Singular Value Decomposition**

If A is a rectangular m £ k matrix of real numbers, then there exists an m £ m orthogonal matrix U and a k £ k orthogonal matrix V such that is an m £ k matrix where the (i, j)th entry i ¸ 0, i = 1 min(m, k) and the other entries are zero The positive constants i are the singular values of A If A has rank r, then there exists r positive constants 1, 2,r, r orthogonal m £ 1 unit vectors u1,u2,,ur and r orthogonal k £ 1 unit vectors v1,v2,,vr such that Similar to the spectral decomposition theorem

12
**Singular Value Decomposition (contd.)**

If A is a symmetric and positive definite then SVD = Eigen decomposition EIG(i) = SVD(i2) Here AAT has an eigenvalue-eigenvector pair (i2,ui) Alternatively, the vi are the eigenvectors of ATA with the same non zero eigenvalue i2

13
**Example for SVD Let A be a symmetric, positive definite matrix**

U can be computed as V can be computed as

14
Example for SVD Taking 21=12 and 22=10, the singular value decomposition of A is Thus the U, V and are computed by performing eigen decomposition of AAT and ATA Any matrix has a singular value decomposition but only symmetric, positive definite matrices have an eigen decomposition

15
**Applications of SVD in Linear Algebra**

Inverse of a n £ n square matrix, A If A is non-singular, then A-1 = (UVT)-1= V-1UT where -1=diag(1/1, 1/1,, 1/n) If A is singular, then A-1 = (UVT)-1¼ V0-1UT where 0-1=diag(1/1, 1/2,, 1/i,0,0,,0) Least squares solutions of a m£n system Ax=b (A is m£n, m¸n) =(ATA)x=ATb ) x=(ATA)-1 ATb=A+b If ATA is singular, x=A+b¼ (V0-1UT)b where 0-1 = diag(1/1, 1/2,, 1/i,0,0,,0) Condition of a matrix Condition number measures the degree of singularity of A Larger the value of 1/n, closer A is to being singular

16
**Applications of SVD in Linear Algebra**

Homogeneous equations, Ax = 0 Minimum-norm solution is x=0 (trivial solution) Impose a constraint, “Constrained” optimization problem Special Case If rank(A)=n-1 (m ¸ n-1, n=0) then x= vn ( is a constant) Genera Case If rank(A)=n-k (m ¸ n-k, n-k+1== n=0) then x=1vn-k+1++kvn with 21++2n=1 Has appeared before Homogeneous solution of a linear system of equations Computation of Homogrpahy using DLT Estimation of Fundamental matrix For proof: Johnson and Wichern, “Applied Multivariate Statistical Analysis”, pg 79

17
What is the use of SVD? SVD can be used to compute optimal low-rank approximations of arbitrary matrices. Face recognition Represent the face images as eigenfaces and compute distance between the query face image in the principal component space Data mining Latent Semantic Indexing for document extraction Image compression Karhunen Loeve (KL) transform performs the best image compression In MPEG, Discrete Cosine Transform (DCT) has the closest approximation to the KL transform in PSNR

18
**Image Compression using SVD**

The image is stored as a 256£264 matrix M with entries between 0 and 1 The matrix M has rank 256 Select r ¿ 256 as an approximation to the original M As r in increased from 1 all the way to 256 the reconstruction of M would improve i.e. approximation error would reduce Advantage To send the matrix M, need to send 256£264 = numbers To send an r = 36 approximation of M, need to send * *264 = numbers 36 singular values 36 left vectors, each having 256 entries 36 right vectors, each having 264 entries Courtesy:

Similar presentations

Presentation is loading. Please wait....

OK

Chapter 13 Discrete Image Transforms

Chapter 13 Discrete Image Transforms

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on human nutrition and digestion test Ppt on porter's five forces model pdf Types of window display ppt online Ppt on object-oriented programming examples Ppt on ethical hacking and information security Ppt on paintings and photographs related to colonial period food Latest backgrounds for ppt on social media Ppt on cost accounting standard Download ppt on mind controlled robotic arms manufacturers Ppt on solar power air conditioning