Linear Algebra, Principal Component Analysis and their Chemometrics Applications.
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Transcript of Linear Algebra, Principal Component Analysis and their Chemometrics Applications.
Linear Algebra, Principal Component Analysis and
their Chemometrics Applications
Linear algebra is the language of chemometrics. One can not expect to truly understand most chemometric techniques without a basic understanding of linear algebra
Linear Algebra
Vector
A vector is a mathematical quantity that is completely described by its magnitude and direction
x1
y1
x
y
P
Vector
A vector is a mathematical quantity that is completely described by its magnitude and direction
x1
y1
x
y
P
P =
x1
y1
MATLAB Notation
Length of a Vector
x1
y1
x
y
PP = x1
2 + y12
x = [x1, x2, …, xn]
x M= ( xi2 ) 0.5
i=1
n
Normal Vector
u =xx
u =1
Normalized vector
Mean Centered Vector
x1
x2
xn
…x = mx = M
xi
i=1
n
nmcx =
x1 - mx
…
x2 - mx
xn - mx
0015100
x = mx = 1
-1-1040-1-1
mcx =
0+20+21+25+21+20+20+2
y =
2237322
=mx = 3
0
0.2
0.4
0.6
0.8
1
1.2
400 450 500 550 600Wavelength (nm)
Ab
so
rba
nc
e
00.20.40.60.8
11.21.4
400 450 500 550 600Wavelength (nm)
Ab
so
rba
nc
e
-0.4
-0.2
0
0.2
0.4
0.6
400 450 500 550 600
Wavelength (nm)
Ab
so
rba
nc
e
Mean centeredMean centered
?
The length of a mean centered vector is proportional to the standard deviation of its elements
y1
y2
yn
…y = y* =
y1 - my
…
y2 - my
yn - myy* ≈ syi
A set of p vectors [x1, x2, …, xp] with same dimension n is linearly independent if the expression:
ci xi = 0Mi=1
p
holds only when all p coefficients ci are zero
Linear Independent Vectors
x1
x2
x3c1x1
c2x2
A vector space spanned by a set of p linearly independent vectors (x1, x2, …, xp) with the same dimension n is the set of all vectors that are linear combinations of the p vectors that span the space
Vector Space
Basis setA set of n vectors of dimension n which are linearly independent is called a basis of an n-dimensional vector space. There can be several bases of the same vector space
A coordinate space can be thought of as being constructed from n basis vectors of unit length which originate from a common point and which are mutually perpendicular
Coordinate Space
Q = P = x1
y1
x1
y1
x
y
P
x1
y1
Q
Vector Multiplication by a Scalar
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
400 450 500 550
Wavelength (nm)
Ab
so
rba
nc
e
x = 1.19 y = 2.38
x
y
y = 2 x
Addition of Vectorsx1
x2
xn
…
y1
y2
yn
…x + y = + =
x1 + y1
…
x2 + y2
xn + yn
x + y
x1
x2
y1
y2y
x
x1 + y1
x2 + y2
x1cx
…ax + ay = + =
x2cx
xncx
y1cy
…
y2cy
yncy
x1cx + y1cy
…
x2cx + y2cy
xncx + yncy
Component 1 Component 2 mixture
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
400 420 440 460 480 500 520 540 560
Wavelength (nm)
Ab
so
rba
nc
e
axay
ax + ay
Subtraction of Vectorsx1
x2
xn
…
y1
y2
yn
…x - y = - =
x1 - y1
…
x2 - y2
xn - yn
x - y
x1
x2
y1
y2y
x
Inner Product (Dot Product)x1
x2
xn
…x . x = xTx = [x1 x2 … xn] = x12 + x2
2 + … +xn2
= x2
x . y = xTy = x y cos
The cosine of the angle of two vectors is equal to the dot product between the normalized vectors:
x . y
x ycos =
y
xx . y = x y
y
xx . y = - x y
y
x x . y = 0
Two vectors x and y are orthogonal when their scalar product is zero
x . y = 0 and x y = 1=
Two vectors x and y are orthonormal