Using Non-Negative Matrix Factorization (NNMF)
4
$begingroup$
I am trying to understand NNMF (Non-Negative Matrix Factorization). This is not a built-in function in Mathematica, but there is a package that implements it, which is refered to in this post. The package is loaded by:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/NonNegativeMatrixFactorization.m"]
The problem that NNMF tries to solve is this: given a matrix $X$, factor it as $W.H$ where $W$ and $H$ both have all positive entries.
But when I try to apply this using the package, I cannot figure out what is happening. First, construct a matrix $x$ -- I build it random, but of low rank (rank 5):
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
So you can see $x$ is 50 by 100, but is of rank only 5. Applying the NNMF command from the package:
{w, h} = GDCLS[x, 5, "MaxSteps" -> 1000];
Dimensions[w]
Dimensions[h]
So we can see that $w.h$ has the same dimensions as $x$. But
Norm[w.h - x]
is very large, so $w.h$ is not a good approximation to $x$.
Thus my questions: why doesn't NNMF seem to work? Am I expecting the wrong thing?
matrix linear-algebra
asked 11 hours ago
bill sbill s
53.7k376153
$endgroup$
add a comment |
4
$begingroup$
I am trying to understand NNMF (Non-Negative Matrix Factorization). This is not a built-in function in Mathematica, but there is a package that implements it, which is refered to in this post. The package is loaded by:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/NonNegativeMatrixFactorization.m"]
The problem that NNMF tries to solve is this: given a matrix $X$, factor it as $W.H$ where $W$ and $H$ both have all positive entries.
But when I try to apply this using the package, I cannot figure out what is happening. First, construct a matrix $x$ -- I build it random, but of low rank (rank 5):
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
So you can see $x$ is 50 by 100, but is of rank only 5. Applying the NNMF command from the package:
{w, h} = GDCLS[x, 5, "MaxSteps" -> 1000];
Dimensions[w]
Dimensions[h]
So we can see that $w.h$ has the same dimensions as $x$. But
Norm[w.h - x]
is very large, so $w.h$ is not a good approximation to $x$.
Thus my questions: why doesn't NNMF seem to work? Am I expecting the wrong thing?
matrix linear-algebra
asked 11 hours ago
bill sbill s
53.7k376153
$endgroup$
4
$begingroup$
Maybexsimply cannot be factored this way? Moreover, it is more realistic to condsider a relative error measure. E.g.,Norm[w.h - x, "Frobenius"]/Norm[x, "Frobenius"]returns0.00326206which is not that bad... WithMaxSteps -> 10000, one can get down to0.00075928or so.
$endgroup$
– Henrik Schumacher
10 hours ago
$begingroup$
If you create x = xL.xR then it for sure can be expressed as w.h, and there is still significant error in the Norm. But maybe you are right, the error is small compared to the size of x.
$endgroup$
– bill s
10 hours ago
1
$begingroup$
@HenrikSchumacher beat me to it! (BTW, the automatic precision goal is 4.)
$endgroup$
– Anton Antonov
10 hours ago
add a comment |
4
4
4
$begingroup$
I am trying to understand NNMF (Non-Negative Matrix Factorization). This is not a built-in function in Mathematica, but there is a package that implements it, which is refered to in this post. The package is loaded by:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/NonNegativeMatrixFactorization.m"]
The problem that NNMF tries to solve is this: given a matrix $X$, factor it as $W.H$ where $W$ and $H$ both have all positive entries.
But when I try to apply this using the package, I cannot figure out what is happening. First, construct a matrix $x$ -- I build it random, but of low rank (rank 5):
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
So you can see $x$ is 50 by 100, but is of rank only 5. Applying the NNMF command from the package:
{w, h} = GDCLS[x, 5, "MaxSteps" -> 1000];
Dimensions[w]
Dimensions[h]
So we can see that $w.h$ has the same dimensions as $x$. But
Norm[w.h - x]
is very large, so $w.h$ is not a good approximation to $x$.
Thus my questions: why doesn't NNMF seem to work? Am I expecting the wrong thing?
matrix linear-algebra
asked 11 hours ago
bill sbill s
53.7k376153
$endgroup$
I am trying to understand NNMF (Non-Negative Matrix Factorization). This is not a built-in function in Mathematica, but there is a package that implements it, which is refered to in this post. The package is loaded by:
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/NonNegativeMatrixFactorization.m"]
The problem that NNMF tries to solve is this: given a matrix $X$, factor it as $W.H$ where $W$ and $H$ both have all positive entries.
But when I try to apply this using the package, I cannot figure out what is happening. First, construct a matrix $x$ -- I build it random, but of low rank (rank 5):
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
So you can see $x$ is 50 by 100, but is of rank only 5. Applying the NNMF command from the package:
{w, h} = GDCLS[x, 5, "MaxSteps" -> 1000];
Dimensions[w]
Dimensions[h]
So we can see that $w.h$ has the same dimensions as $x$. But
Norm[w.h - x]
is very large, so $w.h$ is not a good approximation to $x$.
Thus my questions: why doesn't NNMF seem to work? Am I expecting the wrong thing?
matrix linear-algebra
matrix linear-algebra
asked 11 hours ago
bill sbill s
53.7k376153
asked 11 hours ago
bill sbill s
53.7k376153
edited 7 hours ago
bill s
asked 11 hours ago
bill sbill s
53.7k376153
asked 11 hours ago
bill sbill s
53.7k376153
asked 11 hours ago
bill sbill s
53.7k376153
53.7k376153
4
$begingroup$
Maybexsimply cannot be factored this way? Moreover, it is more realistic to condsider a relative error measure. E.g.,Norm[w.h - x, "Frobenius"]/Norm[x, "Frobenius"]returns0.00326206which is not that bad... WithMaxSteps -> 10000, one can get down to0.00075928or so.
$endgroup$
– Henrik Schumacher
10 hours ago
$begingroup$
If you create x = xL.xR then it for sure can be expressed as w.h, and there is still significant error in the Norm. But maybe you are right, the error is small compared to the size of x.
$endgroup$
– bill s
10 hours ago
1
$begingroup$
@HenrikSchumacher beat me to it! (BTW, the automatic precision goal is 4.)
$endgroup$
– Anton Antonov
10 hours ago
add a comment |
4
$begingroup$
Maybexsimply cannot be factored this way? Moreover, it is more realistic to condsider a relative error measure. E.g.,Norm[w.h - x, "Frobenius"]/Norm[x, "Frobenius"]returns0.00326206which is not that bad... WithMaxSteps -> 10000, one can get down to0.00075928or so.
$endgroup$
– Henrik Schumacher
10 hours ago
$begingroup$
If you create x = xL.xR then it for sure can be expressed as w.h, and there is still significant error in the Norm. But maybe you are right, the error is small compared to the size of x.
$endgroup$
– bill s
10 hours ago
1
$begingroup$
@HenrikSchumacher beat me to it! (BTW, the automatic precision goal is 4.)
$endgroup$
– Anton Antonov
10 hours ago
4
4
$begingroup$
Maybe
x simply cannot be factored this way? Moreover, it is more realistic to condsider a relative error measure. E.g., Norm[w.h - x, "Frobenius"]/Norm[x, "Frobenius"] returns 0.00326206 which is not that bad... With MaxSteps -> 10000, one can get down to 0.00075928 or so.$endgroup$
– Henrik Schumacher
10 hours ago
$begingroup$
Maybe
x simply cannot be factored this way? Moreover, it is more realistic to condsider a relative error measure. E.g., Norm[w.h - x, "Frobenius"]/Norm[x, "Frobenius"] returns 0.00326206 which is not that bad... With MaxSteps -> 10000, one can get down to 0.00075928 or so.$endgroup$
– Henrik Schumacher
10 hours ago
$begingroup$
If you create x = xL.xR then it for sure can be expressed as w.h, and there is still significant error in the Norm. But maybe you are right, the error is small compared to the size of x.
$endgroup$
– bill s
10 hours ago
$begingroup$
If you create x = xL.xR then it for sure can be expressed as w.h, and there is still significant error in the Norm. But maybe you are right, the error is small compared to the size of x.
$endgroup$
– bill s
10 hours ago
1
1
$begingroup$
@HenrikSchumacher beat me to it! (BTW, the automatic precision goal is 4.)
$endgroup$
– Anton Antonov
10 hours ago
$begingroup$
@HenrikSchumacher beat me to it! (BTW, the automatic precision goal is 4.)
$endgroup$
– Anton Antonov
10 hours ago
add a comment |
1 Answer
1
active
oldest
votes
7
$begingroup$
Thank you for using that package!
The stopping criteria is based on relative precision. Find the lines:
....
normV = Norm[V, "Frobenius"]; diffNorm = 10 normV;
If[ pgoal === Automatic, pgoal = 4 ];
While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm/normV > 10^(-pgoal)],
nSteps++;
...
in the implementation code. Note the condition diffNorm/normV > 10^(-pgoal).
Here is an example based on question’s code:
SeedRandom[2343]
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
(* {50, 100} *)
(* 5 *)
Options[GDCLS]
(* {"MaxSteps" -> 200, "NonNegative" -> True,
"Epsilon" -> 1.*10^-9, "RegularizationParameter" -> 0.01,
PrecisionGoal -> Automatic, "PrintProfilingInfo" -> False} *)
AbsoluteTiming[
{w, h} = GDCLS[x, 5, PrecisionGoal -> 3, "MaxSteps" -> 100000];
{Dimensions[w], Dimensions[h]}
]
(* {19.759, {{50, 5}, {5, 100}}} *)
Norm[w.h - x]/Norm[x]
(* 0.000939317 *)
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
$endgroup$
1
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
$endgroup$
– Henrik Schumacher
8 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
1
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
7
$begingroup$
Thank you for using that package!
The stopping criteria is based on relative precision. Find the lines:
....
normV = Norm[V, "Frobenius"]; diffNorm = 10 normV;
If[ pgoal === Automatic, pgoal = 4 ];
While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm/normV > 10^(-pgoal)],
nSteps++;
...
in the implementation code. Note the condition diffNorm/normV > 10^(-pgoal).
Here is an example based on question’s code:
SeedRandom[2343]
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
(* {50, 100} *)
(* 5 *)
Options[GDCLS]
(* {"MaxSteps" -> 200, "NonNegative" -> True,
"Epsilon" -> 1.*10^-9, "RegularizationParameter" -> 0.01,
PrecisionGoal -> Automatic, "PrintProfilingInfo" -> False} *)
AbsoluteTiming[
{w, h} = GDCLS[x, 5, PrecisionGoal -> 3, "MaxSteps" -> 100000];
{Dimensions[w], Dimensions[h]}
]
(* {19.759, {{50, 5}, {5, 100}}} *)
Norm[w.h - x]/Norm[x]
(* 0.000939317 *)
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
$endgroup$
1
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
$endgroup$
– Henrik Schumacher
8 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
1
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
add a comment |
7
$begingroup$
Thank you for using that package!
The stopping criteria is based on relative precision. Find the lines:
....
normV = Norm[V, "Frobenius"]; diffNorm = 10 normV;
If[ pgoal === Automatic, pgoal = 4 ];
While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm/normV > 10^(-pgoal)],
nSteps++;
...
in the implementation code. Note the condition diffNorm/normV > 10^(-pgoal).
Here is an example based on question’s code:
SeedRandom[2343]
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
(* {50, 100} *)
(* 5 *)
Options[GDCLS]
(* {"MaxSteps" -> 200, "NonNegative" -> True,
"Epsilon" -> 1.*10^-9, "RegularizationParameter" -> 0.01,
PrecisionGoal -> Automatic, "PrintProfilingInfo" -> False} *)
AbsoluteTiming[
{w, h} = GDCLS[x, 5, PrecisionGoal -> 3, "MaxSteps" -> 100000];
{Dimensions[w], Dimensions[h]}
]
(* {19.759, {{50, 5}, {5, 100}}} *)
Norm[w.h - x]/Norm[x]
(* 0.000939317 *)
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
$endgroup$
1
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
$endgroup$
– Henrik Schumacher
8 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
1
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
add a comment |
7
7
7
$begingroup$
Thank you for using that package!
The stopping criteria is based on relative precision. Find the lines:
....
normV = Norm[V, "Frobenius"]; diffNorm = 10 normV;
If[ pgoal === Automatic, pgoal = 4 ];
While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm/normV > 10^(-pgoal)],
nSteps++;
...
in the implementation code. Note the condition diffNorm/normV > 10^(-pgoal).
Here is an example based on question’s code:
SeedRandom[2343]
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
(* {50, 100} *)
(* 5 *)
Options[GDCLS]
(* {"MaxSteps" -> 200, "NonNegative" -> True,
"Epsilon" -> 1.*10^-9, "RegularizationParameter" -> 0.01,
PrecisionGoal -> Automatic, "PrintProfilingInfo" -> False} *)
AbsoluteTiming[
{w, h} = GDCLS[x, 5, PrecisionGoal -> 3, "MaxSteps" -> 100000];
{Dimensions[w], Dimensions[h]}
]
(* {19.759, {{50, 5}, {5, 100}}} *)
Norm[w.h - x]/Norm[x]
(* 0.000939317 *)
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
$endgroup$
Thank you for using that package!
The stopping criteria is based on relative precision. Find the lines:
....
normV = Norm[V, "Frobenius"]; diffNorm = 10 normV;
If[ pgoal === Automatic, pgoal = 4 ];
While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm/normV > 10^(-pgoal)],
nSteps++;
...
in the implementation code. Note the condition diffNorm/normV > 10^(-pgoal).
Here is an example based on question’s code:
SeedRandom[2343]
xKer = RandomInteger[{0, 10}, {5, 5}];
xL = RandomInteger[{0, 10}, {50, 5}];
xR = RandomInteger[{0, 10}, {5, 100}];
x = xL.xKer.xR;
Dimensions[x]
MatrixRank[x]
(* {50, 100} *)
(* 5 *)
Options[GDCLS]
(* {"MaxSteps" -> 200, "NonNegative" -> True,
"Epsilon" -> 1.*10^-9, "RegularizationParameter" -> 0.01,
PrecisionGoal -> Automatic, "PrintProfilingInfo" -> False} *)
AbsoluteTiming[
{w, h} = GDCLS[x, 5, PrecisionGoal -> 3, "MaxSteps" -> 100000];
{Dimensions[w], Dimensions[h]}
]
(* {19.759, {{50, 5}, {5, 100}}} *)
Norm[w.h - x]/Norm[x]
(* 0.000939317 *)
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
edited 5 hours ago
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
answered 10 hours ago
Anton AntonovAnton Antonov
23.9k167114
23.9k167114
1
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
$endgroup$
– Henrik Schumacher
8 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
1
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
add a comment |
1
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
$endgroup$
– Henrik Schumacher
8 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
1
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
1
1
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
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– Henrik Schumacher
8 hours ago
$begingroup$
This algorithm seems to be a bit slow. 100000 iterations is quite a lot. I am pretty sure that one can make a semi-smooth Newton method with much higher convergence rate work for the underlying optimization problem. If you like, I can elaborate on this. Are you interested?
$endgroup$
– Henrik Schumacher
8 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
Thanks for writing the package! And of course, thanks also for helping me understand how to use it. I am hoping to replace some SVD calculations with NNMF.
$endgroup$
– bill s
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
$begingroup$
@bills Thanks, good to hear! You might be also interested in Independent Component Analysis (ICA) discussed (together with NNMF) in MSE's question "How to do Independent Component Analysis?", and the Community posts "Independent component analysis for multidimensional signals" and "Comparison of dimension reduction algorithms over mandala images generation".
$endgroup$
– Anton Antonov
7 hours ago
1
1
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
$begingroup$
@HenrikSchumacher 1) Yes, this NNMF implementation is slow and NNMF should be fast (enough) since it is usually run several times, since NNMF is prone to go into local minima. 2) "100000 iterations is quite a lot." -- I mostly use NNMF to NLP (topic extraction) and I rarely run NNMF more than 12-20 steps. 3) Of course, all improvement suggestions are welcome. I would say, it would be best if you write a package and post it in GitHub.
$endgroup$
– Anton Antonov
6 hours ago
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4
$begingroup$
Maybe
xsimply cannot be factored this way? Moreover, it is more realistic to condsider a relative error measure. E.g.,Norm[w.h - x, "Frobenius"]/Norm[x, "Frobenius"]returns0.00326206which is not that bad... WithMaxSteps -> 10000, one can get down to0.00075928or so.$endgroup$
– Henrik Schumacher
10 hours ago
$begingroup$
If you create x = xL.xR then it for sure can be expressed as w.h, and there is still significant error in the Norm. But maybe you are right, the error is small compared to the size of x.
$endgroup$
– bill s
10 hours ago
1
$begingroup$
@HenrikSchumacher beat me to it! (BTW, the automatic precision goal is 4.)
$endgroup$
– Anton Antonov
10 hours ago