ee3j2 data mining ee3j2 data mining

Slide 1EE3J2 Data Mining

EE3J2 Data Mining

Lecture 10 Statistical Modelling

Martin Russell

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Objectives

To review basic statistical modelling To review the notion of probability distribution To review the notion of probability distribution To review the notion of probability density function To introduce mixture densities To introduce the multivariate Gaussian density

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Discrete variables

Suppose that Y is a random variable which can take any value in a discrete set X={x1,x2,…,xM}

Suppose that y1,y2,…,yN are samples of the random variable Y

If cm is the number of times that the yn = xm then an estimate of the probability that yn takes the value xm is given by:

N

cxyPxP m

mnm

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Discrete Probability Mass Function

0

0.05

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0.25

1 2 3 4 5 6 7 8 9

symbol n

P(n

)

Symbol123456789

Total

Num.Occurrences12023190876357

15620391

1098

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Continuous Random Variables

In most practical applications the data are not restricted to a finite set of values – they can take any value in N-dimensional space

Simply counting the number of occurrences of each value is no longer a viable way of estimating probabilities…

…but there are generalisations of this approach which are applicable to continuous variables – these are referred to as non-parametric methods

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Continuous Random Variables

An alternative is to use a parametric model In a parametric model, probabilities are defined by a

small set of parameters Simplest example is a normal, or Gaussian model A Gaussian probability density function (PDF) is

defined by two parameters – its mean and variance

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Gaussian PDF

‘Standard’ 1-dimensional Guassian PDF:– mean =0

– variance =1

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x

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Gaussian PDF

0

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x

a b

P(a x b)

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Gaussian PDF

For a 1-dimensional Gaussian PDF p with mean and variance :

2exp

2

1,|

2xxpxp

Constant to ensure area under curve is 1

Defines ‘bell’ shape

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More examples

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x

=0.1 =1.0

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x

=10.0 =5.0

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Fitting a Gaussian PDF to Data

Suppose y = y1,…,yn,…,yN is a set of N data values

Given a Gaussian PDF p with mean and variance , define:

How do we choose and to maximise this probability?

N

nnypyp

1

,|,|

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Fitting a Gaussian PDF to Data

Poor fitGood fit

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Maximum Likelihood Estimation

Define the best fitting Gaussian to be the one such that p(y|,) is maximised.

Terminology:– p(y|,), thought of as a function of y is the probability

(density) of y

– p(y|,), thought of as a function of , is the likelihood of ,

Maximising p(y|,) with respect to , is called Maximum Likelihood (ML) estimation of ,

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ML estimation of ,

Intuitively:– The maximum likelihood estimate of should be the

average value of y1,…,yN, (the sample mean)

– The maximum likelihood estimate of should be the variance of y1,…,yN. (the sample variance)

This turns out to be true: p(y| , ) is maximised by setting:

N

n

N

nnn y

Ny

N 1 1

21,

1

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Multi-modal distributions

In practice the distributions of many naturally occurring phenomena do not follow the simple bell-shaped Gaussian curve

For example, if the data arises from several difference sources, there may be several distinct peaks (e.g. distribution of heights of adults)

These peaks are the modes of the distribution and the distribution is called multi-modal

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Gaussian Mixture PDFs

Gaussian Mixture PDFs, or Gaussian Mixture Models (GMMs) are commonly used to model multi-modal, or other non-Gaussian distributions.

A GMM is just a weighted average of several Gaussian PDFs, called the component PDFs

For example, if p1 and p2 are Gaussiam PDFs, then

p(y) = w1p1(y) + w2p2(y)

defines a 2 component Gaussian mixture PDF

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Gaussian Mixture - Example 2 component mixture model

– Component 1: =0, =0.1– Component 2: =2, =1– w1 = w2=0.5

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N(0,0.1)

N(2,1)

Mixture

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Example 2

2 component mixture model– Component 1: =0, =0.1– Component 2: =2, =1– w1 = 0.2 w2=0.8

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N(0,0.1)

N(2,1)

Mixture

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Example 3 2 component mixture model

– Component 1: =0, =0.1

– Component 2: =2, =1

– w1 = 0.2 w2=0.8

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N(0,0.1)

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Mixture

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Example 4

5 component Gaussian mixture PDF

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N(0,0.1)

N(2,1)

N(3,0.2)

N(3,0.2)

N(3,0.2)

Mixture

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Gaussian Mixture Model

In general, an M component Gaussian mixture PDF is defined by:

where each pm is a Gaussian PDF and

M

mmm ypwyp

1

M

mmm ww

1

1,10

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Estimating the parameters of a Gaussian mixture model A Gaussian Mixture Model with M components has:

– M means: 1,…,M

– M variances 1,…,M

– M mixture weights w1,…,wM.

Given a set of data y = y1,…,yN, how can we estimate these parameters?

I.e. how do we find a maximum likelihood estimate of 1,…,M, 1,…,M, w1,…,wM?

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Parameter Estimation

If we knew which component each sample yt came from, then parameter estimation would be easy:– Set m to be the average value of the samples which

belong to the mth component– Set m to be the variance of the samples which belong to

the mth component– Set wm to be the proportion of samples which belong to

the mth component But we don’t know which component each sample

belongs to.

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Solution – the E-M algorithm

Guess initial values

For each n calculate the probabilities

Use these probabilities to estimate how much each sample yn ‘belongs to’ the mth component

Calculate:

001

001

001 ,...,,,...,,,..., NNN ww

00 ,| mmnnm ypyp

N

nnnmm y

1,

1

This is a measure of how much yn ‘belongs to’ the mth component

REPEAT

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The E-M algorithm

Parameter set

p(y | )

(0)… (i)

local optimum

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E-M Algorithm

Let’s just look at estimation of a the mean μ of a single component of a GMM

In fact,

In other words, λn is the probability of the mth component given the data point yn

N

nn

N

nnn Ky

K 11

,1

nn ymP |

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E-M continued

From Bayes’ theorem:

M

kknk

mnmnn

wyp

wyp

yp

mPmypymP

1

||

Calculate from mth Gaussian

component

Sum over all components

mth weight

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Example – initial model

P(m1|y6)=λ1

P(m2|y6)=λ2

m1

m2

y6

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Example – after 1st iteration of E-M

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Example – after 2nd iteration of E-M

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Example – after 4th iteration of E-M

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Example – after 10th iteration of E-M

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Multivariate Gaussian PDFs

All PDFs so far have been 1-dimensional They take scalar values But most real data will be represented as D-

dimensional vectors The vector equivalent of a Gaussian PDF is called a

multivariate Gaussian PDF

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Multivariate Gaussian PDFs

Contours of equal probability

1-dimensional

Gaussian PDFs

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Multivariate Gaussian PDFs

1-dimensional

Gaussian PDFs

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Multivariate Gaussian PDF

The parameters of a multivariate Gaussian PDF are:– The (vector) mean – The (vector) variance – The covariance The covariance matrix

yyyp T

p1

2

12

2

1exp

)2(

1

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Multivariate Gaussian PDFs

Multivariate Gaussian PDFs are commonly used in pattern processing and data mining

Vector data is often not unimodal, so we use mixtures of multivariate Gaussian PDFs

The E-M algorithm works for multivariate Gaussian mixture PDFs

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Summary

Basic statistical modelling Probability distributions Probability density function Gaussian PDFs Gaussian mixture PDFs and the E-M algorithm Multivariate Gaussian PDFs