getCMFopts {CMF} | R Documentation |

## 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

```
getCMFopts()
```

### 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 increasing 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 1. |

`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 |

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

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

`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 |

`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 |

`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, 2014.

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

```
CMF_options <- getCMFopts()
CMF_options$iter.max <- 500 # Change the number of iterations from default
# of 200 to 500.
CMF_options$useBias <- FALSE # Do not take row and column means into
# consideration.
# These options will be in effect when CMF_options is passed on to CMF.
```

*CMF*version 1.0.3 Index]