# Default options for CMF

### Description

A helper function that creates a list of options to be
passed to `CMF`

. To run the code with other option
values, first run this function and then directly modify
the entries before passing the list to `CMF`

.

### Usage

1 |

### Details

Most of the parameters are for controlling the
optimization, but some will alter the model itself. In
particular, `useBias`

is used for turning the bias
terms on and off, and `method`

will change the prior
for `U`

.

The default choice for `method`

is `"gCMF"`

,
providing the group-wise sparse CMF that identifies both
shared and private factors (see Klami et al. (2013) for
details). The value `"CMF"`

turns off the group-wise
sparsity, providing a CMF solution that attempts to learn
only factors shared by all matrices. Finally,
`method="GFA"`

implements the group factor analysis
(GFA) method, by fixing the variance of `U[[1]]`

to
one and forcing `useBias=FALSE`

. Then `U[[1]]`

can be interpreted as latent variables with unit variance
and zero mean, as assumed by GFA and CCA (special case of
GFA with M=2). Note that as a multi-view learning method
`"GFA"`

requires all matrices to share the same rows,
the very first entity set.

### Value

Returns a list of:

`init.tau ` |
Initial value for the noise precisions. Only matters for Gaussian likelihood. |

`init.alpha` |
Initial value for the automatic relevance determination (ARD) prior precisions. |

`grad.reg ` |
The regularization parameter for the under-relaxed Newton iterations. 0=no regularization, larger values provide inncreasing regularization. The value must be below 1. |

`gradIter ` |
How many gradient steps for updating the projections are performed during each iteration of the whole algorithm. Default is one. |

`grad.max ` |
Maximum absolute change for the elements of the projection matrices during one gradient step. Small values help to prevent over-shooting, wheres inf results to no constraints. Default is inf. |

`iter.max ` |
Number of iterations for the whole algorithm. |

`computeCost ` |
Should the cost function values be computed or not. Defaults to TRUE. |

`verbose ` |
0 = supress all printing, 1 = print current iteration and test RMSE every now and then, 2 = in addition to level 1 print also the current gradient norm. |

`useBias ` |
Set this to FALSE to exclude the row and column bias terms. The default is TRUE. |

```
method
``` |
Default value of "gCMF" computes the CMF with group-sparsity. The other possible values are "CMF" for turning off the group-sparsity prior, and "GFA" for implementing group factor analysis (and canonical correlation analysis when M=2). |

```
prior.alpha_0
``` |
Hyperprior values for the gamma prior for ARD. |

`prior.alpha_0t ` |
Hyperprior values for the gamma prior for tau. |

### Author(s)

Arto Klami and Lauri VĂ¤re

### References

Arto Klami, Guillaume Bouchard, and Abhishek Tripathi. Group-sparse embeddings in collective matrix factorization. arXiv:1312.5921, 2013.

Seppo Virtanen, Arto Klami, Suleiman A. Khan, and Samuel Kaski. Bayesian group factor analysis. In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, volume 22 of JMLR:W&CP, pages 1269-1277, 2012.

### See Also

'CMF'

### Examples

1 2 3 4 | ```
CMF_options = getCMFopts()
CMF_options$iter.max = 500 #Change the number of iterations from default of 200 to 500.
CMF_options$useBias = FALSE #Don't take row and column means into consideration.
#These options will be in effect when CMF_options is passed on to CMF.
``` |