Non-negative Matrix Factorization
An implementation of non-negative matrix factorization. This can be used to decompose an input dataset into two low-rank non-negative components.
Available Predicates
nmf/13
Initilizes the nmf model and applies it to the given data.
%% part of the predicate definition
nmf( +string,
+integer, +float32,
+pointer(float_array), +integer, +integer,
+integer,
-pointer(float_array), -integer, -integer,
-pointer(float_array), -integer, -integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| updateRule | +string | Update rules for each iteration: "multdist", "multdiv", "als" . | multdist |
| maxIterations | +integer | Number of iterations before NMF terminates (0 runs until convergence. | 10000 |
| minResidue | +float | The minimum root mean square residue allowed for each iteration, below which the program terminates. | 1e-5 |
| data | +matrix | Input matrix to be factorized. | - |
| rank | +integer | Rank r of the factorization. | - |
| W | -matrix | Basis matrix to be output. | - |
| H | -matrix | Encoding matrix to output. | - |
Connected Links/Resources
If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
added some of the links from the python documentation