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Matrices CS485/685 Computer Vision Dr. George Bebis.

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1 Matrices CS485/685 Computer Vision Dr. George Bebis

2 Matrix Operations Matrix addition/subtraction –Matrices must be of same size. Matrix multiplication Condition: n = q m x nq x pm x p

3 Identity Matrix

4 Matrix Transpose

5 Symmetric Matrices Example:

6 Determinants 2 x 2 3 x 3 n x n

7 Determinants (cont’d) diagonal matrix:

8 Matrix Inverse The inverse A -1 of a matrix A has the property: AA -1 =A -1 A=I A -1 exists only if Terminology –Singular matrix: A -1 does not exist –Ill-conditioned matrix: A is close to being singular

9 Matrix Inverse (cont’d) Properties of the inverse:

10 Pseudo-inverse The pseudo-inverse A + of a matrix A (could be non- square, e.g., m x n) is given by: It can be shown that:

11 Matrix trace properties :

12 Rank of matrix Equal to the dimension of the largest square sub- matrix of A that has a non-zero determinant. Example: has rank 3

13 Rank of matrix (cont’d) Alternative definition: the maximum number of linearly independent columns (or rows) of A. Therefore, rank is not 4 ! Example:

14 Rank and singular matrices

15 Orthogonal matrices A is orthogonal if: Notation: Example:

16 Orthonormal matrices A is orthonormal if: Note that if A is orthonormal, it easy to find its inverse: Property:

17 Eigenvalues and Eigenvectors The vector v is an eigenvector of matrix A and λ is an eigenvalue of A if: Interpretation: the linear transformation implied by A cannot change the direction of the eigenvectors v, only their magnitude. (assume non-zero v)

18 Computing λ and v To find the eigenvalues λ of a matrix A, find the roots of the characteristic polynomial: Example:

19 Properties Eigenvalues and eigenvectors are only defined for square matrices (i.e., m = n) Eigenvectors are not unique (e.g., if v is an eigenvector, so is kv) Suppose λ 1, λ 2,..., λ n are the eigenvalues of A, then:

20 Properties (cont’d) x T Ax > 0 for every

21 Matrix diagonalization Given A, find P such that P -1 AP is diagonal (i.e., P diagonalizes A) Take P = [v 1 v 2... v n ], where v 1,v 2,... v n are the eigenvectors of A:

22 Matrix diagonalization (cont’d) Example:

23 Only if P -1 exists (i.e., A must have n linearly independent eigenvectors, that is, rank(A)=n) If A has n distinct eigenvalues λ 1, λ 2,..., λ n, then the corresponding eigevectors v 1,v 2,... v n form a basis: (1) linearly independent (2) span R n Are all n x n matrices diagonalizable?

24 Diagonalization  Decomposition Let us assume that A is diagonalizable, then:

25 Decomposition: symmetric matrices The eigenvalues of symmetric matrices are all real. The eigenvectors corresponding to distinct eigenvalues are orthogonal. A=PDP T = P -1 =P T

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