The FAST Gauss Transform MATH 191 Final Presentation By Group III Akua Agyapong, Adrian Ilie,...
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![Page 1: The FAST Gauss Transform MATH 191 Final Presentation By Group III Akua Agyapong, Adrian Ilie, Jameson Miller, Eli Rosen, Nikolay Stoynov.](https://reader030.fdocuments.net/reader030/viewer/2022032704/56649d405503460f94a1b02a/html5/thumbnails/1.jpg)
The FAST Gauss Transform
MATH 191 Final Presentation
By Group III
Akua Agyapong, Adrian Ilie, Jameson Miller,
Eli Rosen, Nikolay Stoynov
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Discrete Gauss Transform
/||
1
2
)( jk sxN
jjk eqxG
Weight coefficients Source locations
Target locations
=1
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Direct Gauss Transform
x1 x2 x3 x4 xi xM. . . . . .. .
.. .
.
s1
si
sN
. . .
. . .
. . .
. . .
. . .
• Naïve solution: O(NM)
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Direct Gauss Transform
• Simple, but slow algorithm• Pseudo code:
targets[] - array of target pointsresults[] - array of values at target points sources[] - array of source pointsweights[] - array of weights associated with source points
for(int i = 0; i < numTargetPoints; i++) { results[i] = 0; for(int j = 0; j < numSourcePoints; j++){ results[i] += weights[j]* e^(targets[i] - sources[j]) }}
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Fast Gauss Transform
• Less costly algorithm using Numerical Approximation:
2
0
2
P
Pp
Li p x
p
x
eC
e
L
• Interval Length and Number of Coefficients?
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Gaussian
• Approximation– Determine interval length, L
• Error =
• Fourier Series (smooth, periodic function)
– Calculate coefficients
– Optimal number of terms• Determined by excluding extremely small Fourier
coefficients• P=20
2xe
dxeeL
CL
L
Lxpix
p
2/
2/
221
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Evaluation of Fourier Series (1)
n
nkk
n
nkk kxbikxaxf )sin()cos()(
• The result of the evaluation of a Fourier Series is a complex number– C++ has a complex number template in the STL
• Supplies correct implementation of addition, multiplication and other algebraic operations
• No conjugate member function
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Evaluation of Fourier Series (2)
• Since the Gaussian is an even function, the imaginary part drops out
• ai = a-i , so we can combine them into one step
)cos(2
))cos()cos((
1
1
n
kko
n
kkko
kxaa
xkaxkaa
)cos()(
n
nkk kxaxf
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Fast Gauss Transform
• Implementation:
)()(2
)(
P
Pp
Lsxip
p
N
Njjkk
jkk
k
eCqGxG
• Rearrangement:
LipsN
Njj
P
Ppp
Lxip
k
jk
k
k
eqCeG 22
)(
Wpk
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Recursion
• Index shift:
)(
)(
)(
)(
2)(
1
2)(1
2)(
2)(
1
11
11
1
11
1
P
Pp
Lsxip
p
N
Njj
P
Pp
Lsxip
p
N
Njj
P
Pp
Lsxip
p
N
Njj
P
Pp
Lsxip
p
N
Njjk
jkk
k
jkk
k
jkk
k
jkk
k
eCq
eCq
eCq
eCqG
Wpk
Wp-k+1
Wp+k+1
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Sliding the evaluation window
)( 112
1
1
kp
kp
P
Pp
kp
Lxpi
k WWWeGk
Already calculated directly
inf k sup k
inf k+1 sup k+1
xk
xk+1
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Algorithm – initial phase
• Determine inf0 and sup0
• Compute
• Compute
PPpeqCW LSpi
jjpp
j
...,2sup
inf
00
0
LxpiP
Ppp eWG
20
0
0
Total Work: O(1)
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Algorithm – loop phase, i=1..N
• Advance infk and supk to infk+1 and supk+1
• Compute
• Compute
• Compute
• Compute
PPpeqCW LSpi
jjp
kp
jk
k
...,21inf
inf
11
LxpiP
Pp
pkk
k
eWG2
PPpeqCW LSpi
jjp
kp
jk
k
...,2sup
1sup
11
111
kp
kp
kp
kp WWWW
Total Work: O(N)
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Timing comparison
Timing comparison
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
Number of targets
Tim
e (m
s)
O(n2)
Trunc
Appr
O(n)
O(n2) 203 735 2812 10968 43344 173985 687141 3E+06
Trunc 16 46 141 547 2187 8750 34250 238877
Appr 31 79 218 719 2453 9296 35516 239501
O(n) 47 93 204 437 813 1782 3265 10750
512 1024 2048 4096 8192 16384 32768 65536
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Timing comparison (log scale)
Timing comparison (log scale)
0
2
4
6
8
10
12
14
16
Number of targets
log(
Tim
e) (m
s) O(n2)
Trunc
Appr
O(n)
O(n2) 5.3132 6.5999 7.9417 9.3027 10.677 12.067 13.44 14.872
Trunc 2.7726 3.8286 4.9488 6.3044 7.6903 9.0768 10.441 12.384
Appr 3.434 4.3694 5.3845 6.5779 7.8051 9.1373 10.478 12.386
O(n) 3.8501 4.5326 5.3181 6.0799 6.7007 7.4855 8.091 9.2827
512 1024 2048 4096 8192 16384 32768 65536
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Applications
• Option pricing– Determining optimal selling strategy by sum
of Gaussians
Mark Broadie and Yusaku Yamamoto, January 2002
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Applications
• Color tracking– Mixture of Gaussians for modeling regions
with a mixture of color.
Ahmed Elgammal et al, IEEE,Transactions on Pattern Analysis and Machine Intelligence, November 2003
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Recent Developments
• Improved Fast Gauss Transform
FGT has successfully accelerated the kernel density estimation to linear running time for low dimensional problems. However, the cost of a direct extension of the FGT to higher-dimensional grows exponentially with dimension, making it impractical for dimension above 3.
C. Yang, R. Duraiswami, N. A.. Gumerov and L. Davis – ICCV 2003