Introduction to pca v2

Introduction
to
PCA

Christian
Zuniga,
PhD

Friday,
November
8,
2019

Principal
component
analysis
(PCA)
is
an
unsupervised,
linear
technique
for

dimensionality
reduction
first
developed
by
Pearson
in
19011,2,3.
It
is
widely
used
in

many
areas
of
data
mining
such
as
visualization,
image
processing
and
anomaly

detection.
It
is
based
on
the
fact
that
data
may
have
redundancies
in
its

representation.
Data
refers
to
a
collection
of
similar
objects
and
their
features.
An

object
could
be
a
house
and
the
features
the
location,
the
number
of
bedrooms,
the

square
footage,
and
any
other
characteristic
that
can
be
recorded
of
the
house.
In

PCA
analysis,
redundancy
in
the
data
refers
to
linear
correlation
among
features.

Knowledge
of
one
feature
reveals
some
knowledge
of
another
feature.
PCA
may
use

this
redundancy
to
form
a
smaller
set
of
features,
called
principal
components
that

can
approximate
well
the
data.

Figure
1
shows
the
general
idea.
The
data
is
represented
as
a
matrix
X
with
N

objects
(like
houses)
and
F
features
(like
square
footage).
PCA
linearly
transforms

the
features
into
a
new
set
and
retains
the
G
most
relevant
features
where
G
<
F.
The

new
features
are
called
the
principal
components.
The
new
data
matrix
Y
is
Y
=
PX,

where
P
is
a
G
by
F
projection
matrix.

The
first
principal
component
captures
most

of
the
variance
of
the
data.
Each
additional
principal
component
is
made
to
capture

the
remaining
variance
and
is
uncorrelated
or
orthogonal
to
other
principal

components.

Figure
1
PCA
transforms
a
data
matrix
into
a
new
one
with
fewer
features.

The
cars
dataset
from
UC
Irvine
will
be
used
as
an
example4.

This
set
contains
9

features
for
392
cars
of
various
makes
and
models.

Figure
2
shows
two
sample

features,
‘acceleration’
plotted
vs.
‘horsepower’.

Acceleration
is
given
in
the
time

taken
for
a
car
to
accelerate
from
0
to
60
mph.
The
figure
shows
the
two
features

have
opposite
trends,
or
are
negatively
correlated.
This
is
not
surprising
since

higher
horsepower
should
result
in
smaller
times.

Figure
2
Two
features
of
car
data
set
shows
the
data
is
concentrated
along
a
line
P1.

Figure
2
shows
that
most
of
the
variation
of
the
features
is
concentrated
along
a
line

labeled
‘P1’.

The
remainder
of
the
variation
is
along
a
second
line
labeled
‘P2’.
The

lines
can
be
characterized
by
unit
vectors
vj
=
[a1j,
a2j]
(j=1,2)
that
give
the
lines’

orientations.
The
lines’
displacements
from
the
origin
do
not
matter
since
the
data

will
later
be
centered
at
zero.
Each
point
represents
a
car
and
can
also
be

represented
by
a
vector
xi
=
[horsepoweri,
accelerationi],
where
the
subscript

corresponds
to
the
ith
car.
Each
point
can
be
projected
onto
a
line
Pj
by
the
inner

product
of
vj
and
xi
as
shown
in
Figure
3.

𝑝!" = 𝑎!! 𝑎𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛! + 𝑎!!ℎ𝑜𝑟𝑠𝑒𝑝𝑜𝑤𝑒𝑟!

Figure
3
Projecting
a
point
onto
line
P1.

This
new
feature
p1
is
the
first
principal
component
and
is
a
linear
combination
of

the
original
two
features,
horsepower
and
acceleration.

In
general
it
will
not
have
a

more
descriptive
name
but
one
could
be
given
to
clarify
the
concept.
One
option
is
to

think
of
the
combination
of
‘horsepower’
and
‘acceleration’
as
the
‘performance’
of

the
car.

The
question
is
then
how
to
find
the
coefficients
a11,
and
a21
of
vector
v1,
which
gives

the
direction
of
the
best-‐fit
line
P1.
This
line
should
be
as
close
to
all
points
as

possible,
minimizing
the
average
distance
J
to
all
the
points.

𝐽 =
1
𝑁
𝑑!
!
!
!!!

The
solution
lies
in
the
covariance
matrix
of
the
features
SX.
Specifically;
the

eigenvectors
of
SX
give
the
required
vectors
v1
and
v2.
To
calculate
the
covariance

matrix,
the
mean
of
each
feature
is
subtracted
from
each
row.
To
put
the
features
on

a
similar
scale,
they
should
also
be
divided
by
their
standard
deviation.
This
is
done

to
prevent
the
analysis
from
capturing
uninteresting
directions
in
the
data.
After

this
preprocessing
of
the
data
matrix
X,
the
covariance
matrix
is
and
F
by
F
matrix:

𝑺! =
1
𝑁
𝑿𝑿!

The
vector
v1
corresponds
to
the
eigenvector
with
largest
eigenvalue
λM.
Vector
v1

corresponds
to
the
second
smaller
eigenvalue
λm
(λm
<
λM).

𝑺 𝑿 𝒗 𝟏 = 𝜆! 𝒗 𝟏

𝑺 𝑿 𝒗 𝟐 = 𝜆! 𝒗 𝟐

For
the
car
data
set,
using
only
the
features
‘acceleration’
and
‘horsepower’,
the

covariance
matrix
is.

𝑺 𝑿 =
1 −0.69
−0.69 1

The
off-‐diagonal
term
-‐0.69
shows
the
cross-‐covariance
between
horsepower
and

acceleration,
which
is
negative
as
implied
by
Figure
1.
The
diagonal
terms
show
the

auto-‐covariance
of
each
feature
and
have
value
1
because
of
the
pre-‐scaling.

Using
any
linear
algebra
solver
readily
gives
the
eigenvectors
of
the
covariance

matrix.
The
eigenvalues
are
(1.69,
0.31).
Their
sum
is
the
total
variance,
which
is
2.

Vector
v1
is
[0.707,
-‐0.707]
and
captures
84.5%
of
the
total
variance
(1.69/2).
Figure

4
shows
the
resulting
directions
of
v1
and
v2.

Figure
4
Rescaled
data
with
the
direction
of
the
2
principal
components.

The
projection
matrix
P
can
be
made
with
v1
and
v2
as
rows.
If
both
vectors
are

kept,
there
is
no
loss
in
representation.
The
new
representation
would
have
a

covariance
matrix.

𝑺! =
1
𝑁
𝒀𝒀!

𝑺! =
1
𝑁
𝑷𝑿 𝑷𝑿 !
= 𝑷𝑺 𝑿 𝑷!
= 𝚲

Since
P
has
the
eigenvectors
of
Sx
as
rows,
the
right
hand
side
results
in
a
diagonal

matrix
of
the
eigenvalues
of
Sx.

𝑆! =
1.69 0
0 0.31

For
dimensionality
reduction,
only
v1
would
be
used.

The
new
representation,

matrix
Y,
would
have
a
single
feature,
the
first
principle
component
(Y
=
v1X).

PCA
can
be
applied
to
any
number
of
features.
The
car
data
set
has
the
additional

features:
‘cylinders’,
‘displacement’,
and
‘weight’
for
a
total
of
5
features.
Other

features
are
categorical
and
one
‘mpg’
is
usually
the
target
variable
of
interest.
The

same
process
can
be
done
to
obtain
the
principal
components.
Each
principal

component
is
a
linear
combination
of
these
5
features.
Standard
scaling
is
applied
on

the
features
before
making
the
covariance
matrix
Sx
(now
a
5
by
5
matrix).

The

eigenvalues
and
eigenvectors
are
found
and
the
eigenvectors
used
to
make
the

projection
matrix
P.
Figure
5
shows
the
percentage
of
variance
explained
by
each

component.
Again
the
first
component
captures
over
80%
of
the
total
variance.

Instead
of
5
features,
1-‐2
principal
components
may
be
enough
for
various

purposes.

Figure
5
Percentage
of
variance
explained
by
each
principal
component.

For
example,
Figure
6
shows
a
plot
of
principal
component
2
vs.
principal

component
1.
Together
they
capture
about
96%
of
the
total
variance.
The
figure

shows
there
may
be
3
clusters,
or
groups
of
cars.
These
may
correspond
to
different

types
of
cars
such
as
sports
cars,
sedans,
and
trucks.
Confirming
with
the
make
of

the
car
might
clarify
this.
A
clustering
algorithm
like
k-‐means
may
be
applied
to

quantify
the
clusters.
This
shows
a
common
application
of
PCA
in
dimensionality

reduction
where
fewer
features
help
with
many
machine
learning
algorithms.

Figure
6
Principal
Component
2
vs.
1
indicates
there
may
be
around
3
clusters.

To
summarize,
PCA
is
a
linear,
dimensionality
reduction
technique
that
forms
new

features
using
linear
combinations
of
the
original.
These
new
features,
the
principal

components,
maximize
the
total
variance
capture
and
are
uncorrelated
with
each

other.
The
eigenvectors
of
the
covariance
matrix
are
used
to
transform
the
data

matrix.
In
practice
if
there
are
many
features,
forming
the
covariance
matrix
may
be

computationally
expensive
and
an
SVD
of
the
data
matrix
is
used.

[1]
Gilbert
Strang
“Linear
Algebra
and
Learning
from
Data”
Wellesley
Cambridge

Press
2019

[2]
Deisenroth,
et.
Al.
“Mathematics
for
Machine
Learning”
to
be
published

Cambridge
University
Press.
https://mml-‐book.com

[3]
Shlens
“A
Tutorial
on
Principal
Component
Analysis”
2014

https://arxiv.org/abs/1404.1100

[4]
https://archive.ics.uci.edu/ml/datasets/car+evaluation

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