2. Mathematical Preliminaries · 2009-10-06 · Relation between |S| and : Pattern ... Introduction...

2. Mathematical Preliminaries2. Mathematical Preliminaries2. Mathematical Preliminaries

IntroductionPattern Recognition:

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Mathematical PreliminariesMathematicalMathematical PreliminariesPreliminaries

• Random Variables

• Linear Transformations

• Eigenvalues and Eigenvectors

• Orthonormal Transformations

• Matrix Inversion

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Random VariablesRandomRandom VariablesVariables

In statistical pattern recognition, a pattern (input to a PR system) is a d-dimensional feature vector (random variable)

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Random VariablesRandomRandom VariablesVariables

Is fully characterized by its (cumulative) distribution function:

or density function:

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Random VariablesRandomRandom VariablesVariables

• In pattern recognition, we deal with random vectors drawn from different classes• Conditional density of class i (L classes):

• Unconditional density function of x (mixture density function):

is a priori probability of class i

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Random VariablesRandomRandom VariablesVariables

• A posteriori probability of wi given x (Bayes theorem):

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Random VariablesRandomRandom VariablesVariables

• Expected vector (mean):

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Random VariablesRandomRandom VariablesVariables

• Covariance matrix (indicates the dispersion of the distribution):

is

the variance, is

the standard deviation

of

is

the correlation

coefficient between

and

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Random VariablesRandomRandom VariablesVariables

is

the autocorrelation

function

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Random VariablesRandomRandom VariablesVariables

Gaussian (normal) distribution: describes data that cluster around a mean or average

1-d

d>=1

distance function

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Random VariablesRandomRandom VariablesVariables

Normal distribution:• is uniquely characterized by the expected vector and covariance matrix

• The assumption of normality is a reasonable approximation for many real data sets

• However, normality should not be assumed without good justification

• More about normal distribution

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Linear TransformationsLinearLinear TransformationsTransformations

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Linear TransformationsLinearLinear TransformationsTransformations

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Eigenvalues and EigenvectorsEigenvaluesEigenvalues and and EigenvectorsEigenvectors

-

eigenvectors

-

eigenvalues

Write

for all i:

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Eigenvalues and EigenvectorsEigenvaluesEigenvalues and and EigenvectorsEigenvectors

eigenvalue

matrix

eigenvector

matrix

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Eigenvalues and EigenvectorsEigenvaluesEigenvalues and and EigenvectorsEigenvectors

The eigenvectors

corresponding

to two different

eigenvalues

are orthogonal:

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Eigenvalues and EigenvectorsEigenvaluesEigenvalues and and EigenvectorsEigenvectors

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Eigenvalues and EigenvectorsEigenvaluesEigenvalues and and EigenvectorsEigenvectors

There is no correlation in the transformedspace:

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Eigenvalues and EigenvectorsEigenvaluesEigenvalues and and EigenvectorsEigenvectorsEigenvectors

are the principal

axis of the distribution

• We

rotate

the coordinate

system

• There is

no correlation

between

y1

and y2

• λ1

gives

the variance of y1

, λ2 gives

the variance of y2

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WhiteningWhiteningWhitening

After applying the orthonormal transformation, we can add another transformation that will make the covariance matrix equal to I:

Purpose: to change the scales

of principal components in proportion to

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Simultaneous DiagonalizationSimultaneousSimultaneous DiagonalizationDiagonalization

Goal: diagonalize and simultaneously by a linear transformation:

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Orthonormal TransformationsOrthonormalOrthonormal TransformationsTransformations

Properties:a) Eigenvalues are invariant

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Orthonormal TransformationsOrthonormalOrthonormal TransformationsTransformations

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Orthnormal TransformationsOrthnormalOrthnormal TransformationsTransformations

b) Euclidean distance is invariant

Distance in Y-space is the same as inX-space

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Trace of Covariance MatrixTrace of Covariance Trace of Covariance MatrixMatrix

Let’s look at

Sum of diagonal terms of

The trace of is the summation of all eigenvalues and is invariant under anyorthonormal transformation

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Determinant of Covariance MatrixDeterminantDeterminant of Covariance of Covariance MatrixMatrix

The determinant of is equal to the product of all eigenvalues and is invariant under any orthonormal transformation

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Rank of Covariance MatrixRank of Covariance Rank of Covariance MatrixMatrix

Rank of is equal to the number of nonzero eigenvalues

Relation between |S| and :

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Matrix InversionMatrixMatrix InversionInversion

Introduction to matrix inversion: 1 and 2

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Generalized Inverse (Pseudo Inverse)GeneralizedGeneralized Inverse (Pseudo Inverse)Inverse (Pseudo Inverse)

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Generalized Inverse (Pseudo Inverse)GeneralizedGeneralized Inverse (Pseudo Inverse)Inverse (Pseudo Inverse)

However, if matrix is singular someeigenvectors are zero

Generalized (pseudo) inverse:

But: