Description Usage Arguments Details References

NMF algorithms proposed by Kim et al. (2007) that enforces sparsity constraint on the basis matrix (algorithm ‘SNMF/L’) or the mixture coefficient matrix (algorithm ‘SNMF/R’).

1 2 3 4 5 | ```
nmfAlgorithm.SNMF_R(..., maxIter = 20000L, eta = -1,
beta = 0.01, bi_conv = c(0, 10), eps_conv = 1e-04)
nmfAlgorithm.SNMF_L(..., maxIter = 20000L, eta = -1,
beta = 0.01, bi_conv = c(0, 10), eps_conv = 1e-04)
``` |

`maxIter` |
maximum number of iterations. |

`eta` |
parameter to suppress/bound the L2-norm of
If |

`beta` |
regularisation parameter for sparsity
control, which balances the trade-off between the
accuracy of the approximation and the sparseness of
Larger beta generates higher sparseness on |

`bi_conv` |
parameter of the biclustering convergence
test. It must be a size 2 numeric vector
`wminchange` :the minimal allowance of change in row-clusters. `iconv` :decide convergence if row-clusters (within the allowance of `wminchange` ) and column-clusters have not changed for`iconv` convergence checks.
Convergence checks are performed every 5 iterations. |

`eps_conv` |
threshold for the KKT convergence test. |

`...` |
extra argument not used. |

The algorithm ‘SNMF/R’ solves the following NMF
optimization problem on a given target matrix *A* of
dimension *n x p*:

* min_{W,H} 1/2 (|| A - WH ||_F^2 + eta
||W||_F^2 + beta (sum_j ||H[,j]||_1^2))
s.t. W>=0, H>=0 *

The algorithm ‘SNMF/L’ solves a similar problem on
the transposed target matrix *A*, where *H* and
*W* swap roles, i.e. with sparsity constraints
applied to `W`

.

Kim H and Park H (2007). "Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis." _Bioinformatics (Oxford, England)_, *23*(12), pp. 1495-502. ISSN 1460-2059, <URL: http://dx.doi.org/10.1093/bioinformatics/btm134>, <URL: http://www.ncbi.nlm.nih.gov/pubmed/17483501>.

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