# Singular Value Decomposition

## Presentation on theme: "Singular Value Decomposition"— Presentation transcript:

Singular Value Decomposition
Jonathan P. Bernick Department of Computer Science Coastal Carolina University Picture of Metabolic Pathway 1

Outline Derivation Properties of the SVD Applications
Research Directions

Matrix Decompositions
Definition: The factorization of a matrix M into two or more matrices M1, M2,…, Mn, such that M = M1M2…Mn. Many decompositions exist… QR Decomposition LU Decomposition LDU Decomposition Etc. One is special…

Theorem One [Will] For an m by n matrix A:nm and any orthonormal basis {a1,...,an} of n, define (1) si = ||Aai|| (2) Then…

Theorem One (continued)
Proof:

Theorem Two [Will] For an m by n matrix A, there is an orthonormal basis {a1,...,an} of n such that for all i  j, Aai  Aaj = 0 Proof: Since ATA is symmetric, the existence of {a1,...,an} is guaranteed by the Spectral Theorem. Put Theorems One and Two together, and we obtain…

Singular Value Decomposition
[Strang]: Any m by n matrix A may be factored such that A = UVT U: m by m, orthogonal, columns are the eigenvectors of AAT V: n by n, orthogonal, columns are the eigenvectors of ATA : m by n, diagonal, r singular values are the square roots of the eigenvalues of both AAT and ATA

SVD Example From [Strang]:

SVD Properties U, V give us orthonormal bases for the subspaces of A:
1st r columns of U: Column space of A Last m - r columns of U: Left nullspace of A 1st r columns of V: Row space of A 1st n - r columns of V: Nullspace of A IMPLICATION: Rank(A) = r

Application: Pseudoinverse
Given y = Ax, x = A+y For square A, A+ = A-1 For any A… A+ = V-1UT A+ is called the pseudoinverse of A. x = A+y is the least-squares solution of y = Ax.

Rank One Decomposition
Given an m by n matrix A:nm with singular values {s1,...,sr} and SVD A = UVT, define U = {u1| u2| ... |um} V = {v1| v2| ... |vn}T Then… A may be expressed as the sum of r rank one matrices

Matrix Approximation Let A be an m by n matrix such that Rank(A) = r
If s1  s2  ...  sr are the singular values of A, then B, rank q approximation of A that minimizes ||A - B||F, is Proof: S. J. Leon, Linear Algebra with Applications, 5th Edition, p. 414 [Will]

Application: Image Compression
Uncompressed m by n pixel image: m×n numbers Rank q approximation of image: q singular values The first q columns of U (m-vectors) The first q columns of V (n-vectors) Total: q × (m + n + 1) numbers

Example: Yogi (Uncompressed)
Source: [Will] Yogi: Rock photographed by Sojourner Mars mission. 256 × 264 grayscale bitmap  256 × 264 matrix M Pixel values  [0,1] ~ numbers

Example: Yogi (Compressed)
M has 256 singular values Rank 81 approximation of M: 81 × ( ) = ~  numbers

Example: Yogi (Both)

Application: Noise Filtering
Data compression: Image degraded to reduce size Noise Filtering: Lower-rank approximation used to improve data. Noise effects primarily manifest in terms corresponding to smaller singular values. Setting these singular values to zero removes noise effects.

Example: Microarrays Source: [Holter]
Expression profiles for yeast cell cycle data from characteristic nodes (singular values). 14 characteristic nodes Left to right: Microarrays for 1, 2, 3, 4, 5, all characteristic nodes, respectively.

Research Directions Latent Semantic Indexing [Berry] Pseudoinverse
SVD used to approximate document retrieval matrices. Pseudoinverse Applications to bioinformatics via Support Vector Machines and microarrays.

References [Berry]: Michael W. Berry, et. al., “Using Linear Algebra for Intelligent Information Retrieval,” CS , Department of Computer Science, University of Tennessee, Submitted to SIAM Review. [Holter]: Neal S. Holter, et. al., “Fundamental patterns underlying gene expression profiles: Simplicity from complexity,” Proc. Natl. Acad. Sci. USA, /pnas , 2000 (preprint). Available online at

References (continued)
[Strang]: Gilbert Strang, Linear Algebra and Its Applications, 3rd edition, Academic Press, Inc., New York, 1988. [Will]: Todd Will, “Introduction to the Singular Value Decomposition,” Davidson College,

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