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PLDS
The Penalized Linear Dynamical System Project
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Modify the kalman filtering part to reduce duplicate computation
#76
yangyutu
closed
9 years ago
0
update text to reflect new times
#75
jovo
closed
9 years ago
3
plot accuracy & time vs. iteration #
#74
jovo
opened
9 years ago
5
C'*inv(R)
#73
jovo
closed
9 years ago
0
read jovo's fast non-negative paper
#72
jovo
closed
9 years ago
0
profile KFS code
#71
jovo
closed
9 years ago
0
add everyone's affiliations
#70
jovo
closed
9 years ago
3
fig 9
#69
jovo
closed
9 years ago
1
i don't quite understand the SVD method
#68
jovo
closed
9 years ago
0
"as there is no way that we can predict the two noise terms in the model, "
#67
jovo
closed
9 years ago
1
" The prediction accuracies are shown in Figure 8. "
#66
jovo
closed
9 years ago
0
The first N = 1000 are picked as train data,
#65
jovo
closed
9 years ago
0
(Descriptions of data and citations required here)
#64
jovo
closed
9 years ago
0
"As an example, the blue region above accurately matches the DM (dorselmedical) parcel of the five-region parcellation proposed by Nebel MB et al. [18]."
#63
jovo
closed
9 years ago
0
Table 2: Similarities Among Estimated A Matrices
#62
jovo
closed
9 years ago
1
define Amari distance
#61
jovo
closed
9 years ago
0
fig 6
#60
jovo
closed
9 years ago
0
denote the A matrix estimation for the first scan of subject one as A11
#59
jovo
closed
9 years ago
1
et. al should be et al.
#58
jovo
closed
9 years ago
0
"Tuning parameters: λA = 0.00001, λC = 0.00001
#57
jovo
closed
9 years ago
0
"The PLDS "
#56
jovo
closed
9 years ago
1
"and xt contains the mixing coefficients"
#55
jovo
closed
9 years ago
1
"Each column of the C matrix is interpreted as a time-invariant brain network."
#54
jovo
closed
9 years ago
2
figure captions are not sufficiently detailed or explanatory
#53
jovo
closed
9 years ago
1
in 5.2 "PLDS model"
#52
jovo
closed
9 years ago
0
fig 1b
#51
jovo
closed
9 years ago
1
As a demonstrate,
#50
jovo
closed
9 years ago
0
title of fig 1b has typo
#49
jovo
closed
9 years ago
0
Fig 2
#48
jovo
closed
9 years ago
0
"In addition, when the penalties are getting larger, the penalized algorithm’s estimations should become worse."
#47
jovo
closed
9 years ago
0
"with some proper penalty parameters"
#46
jovo
closed
9 years ago
0
The calculation of d(A, B) is essentially a linear assignment
#45
jovo
closed
9 years ago
1
d(A,B)
#44
jovo
closed
9 years ago
4
some distance measure should be first defined.
#43
jovo
closed
9 years ago
1
"The parameter are generated in a similar manner."
#42
jovo
closed
9 years ago
1
Then the sample is sorted in ascending order
#41
jovo
closed
9 years ago
0
Eigen values is 1 word
#40
jovo
closed
9 years ago
0
The A matrix is generated such that the conditional number is no less than some threshold, 50 being used.
#39
jovo
closed
9 years ago
0
5.1 Parameter Estimations
#38
jovo
closed
9 years ago
0
First, a sparse matrix is used to represent R.
#37
jovo
closed
9 years ago
1
The complete EM algorithm for PLDS is addressed as follows.
#36
jovo
closed
9 years ago
0
step 0 in Appendix is wrong?
#35
jovo
closed
9 years ago
1
Appendix 1: Algorithm Kalman Filter Smoother
#34
jovo
closed
9 years ago
0
Expectations (6) are calculated with a Kalman filter/smoother, which is detailed in Appendix 1.
#33
jovo
closed
9 years ago
0
EM pseudocode
#32
jovo
closed
9 years ago
0
With FISTA and L, matrix A can be update:
#31
jovo
closed
9 years ago
0
FISTA is an accelerated version of the Iterative Shrinkage- Threshholding Algorithm (ISTA
#30
jovo
closed
9 years ago
0
swap left and right sides of eq 11
#29
jovo
closed
9 years ago
0
Last but not least, let’s look at parameter A.
#28
jovo
closed
9 years ago
0
consider defining the 'vec' operator
#27
jovo
closed
9 years ago
0
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