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Part 3 Vector Quantization and Mixture Density Model
CSE717, SPRING 2008
CUBS, Univ at Buffalo
Vector Quantization
Quantization Represents continuous range of values by a
set of discrete values Example: floating-point representation of real
numbers in computer Vector Quantization
Represent a data space (vector space) by discrete set of vectors
Vector Quantizer
A mapping from vector space onto a finite subset of the vector space
Y = y1,y2,…,yN finite subset of IRk, referred to as the codebook of Q
Q is usually determined by training data
kk YQ IRIR:
Q
1y2y
3y
Partition of Vector Quantizer
The vector space is partitioned into N cells by the vector quantizer
})(|IR{)(1i
kii yxQxyQR
Q
1y2y
3y
1R 2R
3R
Properties of Partition
Vector quantizer Q defines a complete and disjoint partition of IRk into R1,R2,…,RN
)(, and IR1
jijiRRR jik
N
ii
Quantization Error
Quantization Error for single vector x
is a suitable distance measure Overall Quantization Error
))(,()|( xQxdQx
)|( d
k
dxxpxQxd
XQXdEQXEQ
X
IR
)())(,(
))}(,({)}|({)(
Nearest-Neighbor Condition
The minimum quantization error of a given codebook Y is given by partition
}),(min),(|{Yy
ii yxdyxdxR
x
y1 y2
y3
1
321 ),(),(),(
Rx
yxdyxdyxd
Centroid Condition
Centroid of a cell Ri
is minimized by choosing
as the codebook For Euclidean distance d
}|),({minarg)(cent iRy
i RXyXdERi
i
i
RRXXii dxxxpRXXER )(}|{)(cent |
)}(cent),...,({cent 1 NRRY
)(Q
Centroid
Vector Quantizer Design – General Steps
1. Determine initial codebook Y0
2. Adjust partition of sample data for the current codebook Ym using nearest-neighbor condition
3. Update the codebook Ym →Ym+1 using centroid condition
4. Check a certain condition of convergence. If it converges, return the current codebook Ym+1; otherwise go to step 2
my1
my2
my3
mR1mR2
mR3
},,{ 03
02
01 yyyY
111
mm yy1
22 mm yy
133
mm yy
mR1mR2
mR3
Converge?Y
N
Lloyd’s Algorithm
Lloyd’s Algorithm (Cont)
LBG Algorithm
LBG Algorithm (Cont)
k-Means Algorithm
k-Means Algorithm (Cont)
Mixture Density Model
A mixture model of N random variables X1,…,XN is defined as follows:
is a random variables defined on N labels
},...,1{ NC
N
iiXCiIX
1
),(
function Impulse otherwise ,0
if ,1),(
ji
jiI
,...2,1 21 XXCXXC
Mixture Density Model
Suppose the p.d.f.’s of X1,…,XN are
and
then
)()(
1
xpxpiX
N
iiX
)(),...,(1
xpxpNXX
iiC )Pr(
Example: Gaussian Mixture Model of Two Components
)1Pr(},1,0{),1( CI
),(~),,(~ 2222
2111 NXNX
21 )1( XXX
Histogram of samples Mixture Density
Estimation of Gaussian Mixture Model ML Estimation (Value X and label are given)
Samples in the format of
(-0.39, 0), (0.12, 0), (0.94, 1), (1.67, 0), (1.76, 1), …
S1 (Subset of ): (0.94, 1), (1.76, 1), …
S2 (Subset of ): (-0.39, 0), (0.12, 0), (1.67, 0), …
211
Estimation ML
1 ˆ,ˆ S 222
Estimation ML
2 ˆ,ˆ S
|)||/(|||ˆ 211 SSS
21 )1( XXX
),( kkx
1k 0k
]ˆ,ˆ,ˆ,ˆ,ˆ[ˆ 222
211 θ
Estimation of Gaussian Mixture Model EM Algorithm (Value X is given, label is unknown)
1. Choose initial values of
2. E-Step:
For each sample xk, label is missing. But we can estimate
using its expected value
Samples in the format of ; is missing
(-0.39), (0.12), (0.94), (1.67), (1.76), …
)ˆ,ˆ;()ˆ1()ˆ,ˆ;(ˆ
)ˆ,ˆ;(ˆ
)ˆ,|1Pr(
}ˆ,|{
222
211
211
21
1
kXkX
kX
kk
kk
xpxp
xp
x
xE
θ
θ
k
k
]ˆ,ˆ,ˆ,ˆ,ˆ[ˆ 222
211 θ
x1
x2
)ˆ,|1Pr()ˆ,|1Pr( 2211 θθ xx
)( kx k
Estimation of Gaussian Mixture Model EM Algorithm (Value X is given, label is unknown)
3. M-Step:
We can estimate again using the labels
estimated in the E-Step: }ˆ,|{ˆ θkkk xE
kk
kkk
kk
kkk xx
ˆ
)ˆ(ˆ
ˆ,ˆ
ˆ
ˆ
21
11
]ˆ,ˆ,ˆ,ˆ,ˆ[ˆ 222
211 θ
kk
kkk
kk
kkk xx
)ˆ1(
)ˆ)(ˆ1(
ˆ,)ˆ1(
)ˆ1(
ˆ
22
22
))ˆ1(ˆ/(ˆˆ k
kk
kk
k
Estimation of Gaussian Mixture Model EM Algorithm (Value X is given, label is unknown)
4. Termination:
The log likelihood of n samples
At the end of m-th iteration, if
terminate; otherwise go to step 2 (the E-Step).
n
kkXkXn xpxpxxl
1
222
2111 )]ˆ,ˆ;()ˆ1()ˆ,ˆ;(ˆlog[),...,(
22
thrxxlxxlxxl nm
nm
nm ),...,(/|),...,(),...,(| 111
1