# CS代考 MAST 90138: MULTIVARIATE STATISTICAL TECHNIQUES – cscodehelp代写

MAST 90138: MULTIVARIATE STATISTICAL TECHNIQUES

See Ha ̈rdle and Simar, chapter 11.

5 PRINCIPAL COMPONENT ANALYSIS

5.1 INTRODUCTION

Visualizing 1, 2 or 3 dimensional data is relatively easy: use scatterplot.

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Lecture notes originally by Prof. 1

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When the data are in higher dimension, it is very difficult to visualize them.

Can we find a way to summarise the data?

Summaries should be easier to represent graphically.

Summaries should still contain as much information as possible about the original data.

Often we can achieve this through dimension reduction.

Lecture notes originally by Prof. 2

Toy example: how to reduce to 1 dimension the following 2-dimensional data.

Data:acollectionofi.i.d.pairs(Xi1,Xi2)T ∼(μ,Σ),fori=1,…,n,shown in the scatter plot.

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Lecture notes originally by Prof. 3

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The first thing usually done in these problems is to center the data (easier to understand the geometry for centered data). For for i = 1, . . . , n, we replace (Xi1, Xi2)T by (Xi1 − X ̄1, Xi2 − X ̄2)T :

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Unless otherwise specified, for the rest of this chapter, to avoid heavy no- tation when we refer to Xij we mean Xij − X ̄j.

To reduce these data to a single dimension we could for example keep only the first component Xi1 of each data point.

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Lecture notes originally by Prof. 5

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Keeping only the first component

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X1

Lecture notes originally by Prof. 6

Not very interesting: lose all the information about the second com- ponent X2.

Suppose the data contain the age (X1) and the height (X2) of n = 100 individuals. This amounts to keeping only the age and drop com- pletely the data about height.

Why not instead create a new variable that contains information about both age and height?

Lecture notes originally by Prof. 7

Simple approach: take a linear combination of the age and the height.

For i = 1,…,n we could create a new variable Yi = agei/2 + heighti/2,

i.e. the average of the age and the height. 1/2 and 1/2 are the weights of age and height, respectively.

We often prefer to rescale linear combinations so that the sum of the square of the weights equals 1, for example,

√√

Yi = agei/ 2 + heighti/ 2.

Lecture notes originally by Prof. 8

The values

√√

Yi =Xi1/ 2+Xi2/ 2:

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(X1+X2)/sqrt 2

Lecture notes originally by Prof. 9

Taking a scaled average of the two components = projecting the data onto the 45 degree line (red) and keeping only the projected values.

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How was this figure constructed?

Lecture notes originally by Prof. 10

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Recap: the projection px of a vector x, onto a vector y, is the vector px = xTyy,.

f equals the length of the base of a triangle (jjp jj) divided

potenuse (jjxjj). Hence, we havepx = jx>yj

xTy x ∥y∥

y ∥y∥

Another more transparent way of viewing this: 0, then the angle is equal to 2 . From trigonometry, we

∥y∥2

73

the projection jjpxjj D jjxjjjcosj D kyk ; (“projected value”) (2.42)

tion of x on y (which is defined below). It is the coordinate e Fig. 2.5.

length of the projection

unit vector

in the direction of

e defined with respect to a general metric A

Lecture notes originally by Prof. 11

D

y

c e

b

√√

The linear combination Yi = Xi1/ 2 + Xi2/ 2 is the same as Y i = X iT a

where

Xi=(Xi1,Xi2)T, a=(1/√2,1/√2)T.

So Yi is the projection value (length of the projection vector) of Xi

onto the 45 degree line passing through the origin.

Lecture notes originally by Prof. 12

The the line passing through the origin and a = (1/√2, 1/√2)T is shown in red. The projection of each Xi on that line is shown in blue.

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X1

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(X1+X2)/sqrt 2

Lecture notes originally by Prof. 14

X2

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Instead of giving equal weight to each component of Xi, when re- ducing dimension we would like to lose as little information about the original data as possible.

How we define “lose information” ?

In principal component analysis (PCA), we reduce dimension by

projecting the data onto lines.

Moreover, in PCA, “lose as little information as possible” is defined

as “keep as much of the variability of the original data as possible”. In our two dimensional case example, when choosing the projection

Yi = XiTa on a line, this means we want to find a such that var(Yi)

is as large as possible.

Lecture notes originally by Prof. 15

Why do we want to maximise variance? Here is an example where the projected data are not variable: project the data on the red line

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Lecture notes originally by Prof.

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Projected data land on the same point and have zero variance; don’t learn anything about the data.

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univariate projection

Lecture notes originally by Prof.

17

Recall in the example, we’ve projected onto the 45-degree line through the origin:

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However we would have kept more information if we had instead pro- jected the data on the following line:

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Indeed, on this line, the projected data are more variable than on the pre- vious line.

Lecture notes originally by Prof. 20

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The scaled average (in red) is less variable than the last suggested pro- jected values:

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* scaled ave

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univariate projection

The projections in blue is in fact the one that maximises the variance of the projected values of the data.

Lecture notes originally by Prof. 21

5.2 PCA

Formally, in PCA, when reducing the p-variate Xi’s to univariate Yi1’s, for i = 1,…,n, where the Xi’s are i.i.d.∼ (0,Σ), the goal is to find the linear combination

Yi1 =a1Xi1 +…+apXip =XiTa, where a = (a1,…,ap)T is such that

and

is as large as possible.

p

∥ a ∥ 2 = a 2j = 1

j=1

var(Yi1)

We use Yi1 instead of Yi because there will be more than one projec- tion.

The constraint on a is a scaling factor that makes things easier.

Lecture notes originally by Prof. 22

Let γ1, . . . , γp denote the p unit-length eigenvectors (i.e., ∥γj ∥ = 1) of the covariance matrix Σ, respectively associated with the eigenvalues

λ1≥λ2≥ . . . ≥λp.

Recall: γj’s are only defined up to a change of sign, so each γj can

be replaced by −γj.

It can be shown that the a that maximises the variance is equal to

γ1,

the eigenvector with largest eigenvalue (“first eigenvector”) .

The variable

is called the first principal component of Xi (or “PC1” for short).

Yi1 =a1Xi1 +…+apXip =aTXi =γ1TXi

Lecture notes originally by Prof. 23

More generally, if the data are i.i.d.∼ (μ, Σ) and not already cen- tered,

Yi1 =γ1T{Xi −E(Xi)}=γ1T(Xi −μ) is called the first principal component of Xi.

It is the linear projection of the data that has maximum variance. We always center the data before projecting.

Lecture notes originally by Prof. 24

In PCA, once we have found a univariate projection, how do we add a second projection?

One possibility: on the good old 45-degree line (blue below)

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Those two projections are essentially redundant, we don’t learn much more:

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Lecture notes originally by Prof. 26

proj 2

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We should project onto a line as different as possible, to learn com- plementary information. How?

Project onto a perpendicular direction to that of the PC1. The vari- able obtained is called the second principal component (“PC2” for short).

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The data projected on the two lines are just the same as the original data, but where the axes have been rotated to match the blue and the red lines.

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PC1

Lecture notes originally by Prof. 28

PC2

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More generally, when we transform p-dimensional data into q ≤ p-dimensional Xi’s that are ∼ (μ, Σ), with the γj ’s and λj ’s as defined at page 23,

We start by taking the first principal component of Xi Yi1 =γ1T{Xi −E(Xi)}=γ1T(Xi −μ)

where γ1 the eigenvec of Σ corresponding to the largest eigenval, λ1.

Then for k = 2,…,q, we take the kth principal component of Xi

Yik =γkT{Xi −E(Xi)}=γkT(Xi −μ) (1)

where γk is the evec of Σ corresponding to the kth largest eval, λk.

γj’s are orthonormal ⇒ the projection directions are orthogonal to each other.

Lecture notes originally by Prof. 29

In matrix notation, letting Yi = (Yi1,…,Yip)T and Γ = [γ1|…|γp], we have

Yi = ΓT (Xi − μ).

Suppose we construct Yi1, . . . , Yip as described above. Then we have

E(Yij) = 0, for j = 1,…,p

var(Yij) = λj, for j = 1,…,p

cov(Yik,Yij)=0, k̸=j

var(Yi1) ≥ var(Yi2) ≥ . . . ≥ var(Yip) p

var(Yij) = tr(Σ) j=1

p

var(Yij) = |Σ|.

j=1

Lecture notes originally by Prof. 30

It can be proved that:

– it is not possible to construct a linear combination

Vi = XiT a where ∥a∥ = 1 which has larger variance than λ1 = var(Yi1).

– if we take a variable

Vi = XiT a where ∥a∥ = 1

which is not correlated with the first k PCs of Xi, then the variance

of Vi is maximised by taking Vi = Yi,k+1, the (k + 1)-th PC of Xi.

With all these properties, the hope is that we can gather as much in- formation as possible about the original data by projecting them onto the first few PCs. (q much less than p if p is large)

Lecture notes originally by Prof. 31

5.3 IN PRACTICE

In practice: we do not know Σ nor μ = E(Xi). Instead we use their

empirical counterparts S and X ̄ , i.e.:

We start by taking the first principal component of Xi

Y i 1 = γ 1T ( X i − X ̄ )

where γ1 is the eigenvec of S corresponding to the largest eigen-

value, λ1.

Then for k = 2,…,q, we take the kth principal component of Xi

Yik = γkT (Xi − X ̄ )

where γk is the eigenvector of S corresponding to the kth largest eigenvalue, λk.

Lecture notes originally by Prof. 32

In matrix notation, letting

Yi =(Yi1,…,Yip)T [ap-vector]

and

we have

Y = (Y1,…,Yn)T, [an n-by-p matrix] Y = ( X − 1 n X ̄ T ) Γ

for Γ = [γ1|…|γp].

Once we have computed the PC’s we can:

∗ plot them to see if we can detect clusters

∗ see influential observations (outliers)

∗ see if we can get any insight about the data.

When we detect something in the PC plots, we can:

∗ go back to the original data and try to make the connection,

∗ and check if our interpretation seems correct.

Lecture notes originally by Prof. 33

Example: Swiss bank notes data.

Data: variables measured on 200 Swiss 1000-franc banknotes, of

which 100 were genuine and 100 were counterfeit.

(Source: Flury, B. and Riedwyl, H. (1988). Multivariate Statistics: A practical approach. London: Chapman & Hall, Tables 1.1 and 1.2, pp. 5–8.)

Found in the R package mclust by typing data(banknote). The variables measured are:

X1: Length of bill (mm)

X2: Width of left edge (mm) X3: Width of right edge (mm) X4: Bottom margin width (mm) X5: Top margin width (mm) X6: Length of diagonal (mm)

The first 100 banknotes are genuine and the next 100 are counterfeit. Lecture notes originally by Prof. 34

Scatterplots:

Length

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Diagonal

Lecture notes originally by Prof.

35

138 141 7 9 12 129.0 131.0

8 10 129.0 131.0 214.0 216.0

In R, read the data and produce the scatterplots:

library(mclust)

data(banknote)

StatusX=banknote[,1]

plot(banknote[,2:7])

Status contains the info about whether a note is genuine or counterfeit. Center the data and perform the PC analysis:

XCbank=scale(banknote[, 2:7], scale = FALSE)

PCX=prcomp(XCbank,retx=T)

PCX

Lecture notes originally by Prof. 36

Let’s take a closer look at the two PCs for the banknote data.

The eigenvalues and eigenvectors are given in R in the following form:

Standard deviations (1, .., p=6):

[1] 1.7321388 0.9672748 0.4933697 0.4412015 0.2919107 0.1884534

Rotation (n x k) = (6 x 6):

PC1 PC2 PC3 PC4 PC5 PC6

Length

Left

Right

Bottom -0.768 0.563 0.218 0.186 -0.100 -0.022

Top -0.202 -0.659 0.557 0.451 -0.102 -0.035

Diagonal 0.579 0.489 0.592 0.258 0.084 -0.046

0.044 -0.011 0.326 -0.562 -0.753 0.098

-0.112 -0.071 0.259 -0.455 0.347 -0.767

-0.139 -0.066 0.345 -0.415 0.535 0.632

The eigenvectors are the columna of the so called rotation matrix and the eigenvalues are the square of the so-called standard deviations.

Keep eigenvectors in gamma and eigenvalues in lambda. gamma=PCX$rotation

lambda=PCX$sdevˆ2

Lecture notes originally by Prof. 37

Let’s looks at the first 2 PCs: the data clearly separate into two groups

pX=XCbank%*%gamma

plot(pX[,1],pX[,2],pch=”*”,xlab=”PC1″,ylab=”PC 2″,asp=1)

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PC1

Lecture notes originally by Prof. 38

PC 2

−3 −2 −1 0 1 2 3

Can do more simply in R. Keep projected data Yi’s in Y Y=PCX$x

plot(Y[,1],Y[,2],pch=”*”,xlab=”PC1″,ylab=”PC 2″,asp=1)

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PC1

Lecture notes originally by Prof. 39

PC 2

−3 −2 −1 0 1 2 3

The two groups actually correspond to the genuine and the fake ban- knotes. The first two PCs have captured that information! We don’t need to keep all 6 dimensions to see this. (The blues tend to have large PC1 and PC2 values)

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PC1

Lecture notes originally by Prof. 40

PC 2

−3 −2 −1 0 1 2 3

We have

Yi1 = 0.044Xi1 − 0.112Xi2 − 0.139Xi3 − 0.768Xi4 − 0.202Xi5 + 0.579Xi6 Yi2 = −0.011Xi1 − 0.071Xi2 − 0.066Xi3 + 0.563Xi4 − 0.659Xi5 + 0.489Xi6.

Thus

• the first PC is roughly the difference between the 6th (length of diago- nal) and the 4th component (bottom margin);

• the second PC is roughly the difference between the 5th (top margin) and the sum of the 6th (length of diagonal) and the 4th component (bottom margin).

Lecture notes originally by Prof. 41