NTD | R Documentation |

The input data is assumed to be non-negative tensor. NTD decompose the tensor to the dense core tensor (S) and low-dimensional factor matices (A).

```
NTD(X, M=NULL, pseudocount=.Machine$double.eps, initS=NULL, initA=NULL,
fixS=FALSE, fixA=FALSE, L1_A=1e-10, L2_A=1e-10,
rank = rep(3, length=length(dim(X))),
modes = seq_along(dim(X)),
algorithm = c("Frobenius", "KL", "IS", "Pearson", "Hellinger", "Neyman",
"HALS", "Alpha", "Beta", "NMF"), init = c("NMF", "ALS", "Random"),
nmf.algorithm = c("Frobenius", "KL", "IS", "Pearson", "Hellinger", "Neyman",
"Alpha", "Beta", "ALS", "PGD", "HALS", "GCD", "Projected", "NHR", "DTPP",
"Orthogonal", "OrthReg"),
Alpha = 1,
Beta = 2, thr = 1e-10, num.iter = 100, num.iter2 = 10, viz = FALSE,
figdir = NULL, verbose = FALSE)
```

`X` |
K-order input tensor which has I_1, I_2, ..., and I_K dimensions. |

`M` |
K-order mask tensor which has I_1, I_2, ..., and I_K dimensions. If the mask tensor has missing values, specify the element as 0 (otherwise 1). |

`pseudocount` |
The pseudo count to avoid zero division, when the element is zero (Default: Machine Epsilon). |

`initS` |
The initial values of core tensor which has I_1, I_2, ..., and I_K dimensions (Default: NULL). |

`initA` |
A list containing the initial values of K factor matrices (A_k, <Ik*Jk>, k=1..K, Default: NULL). |

`fixS` |
Whether the core tensor S is updated in each iteration step (Default: FALSE). |

`fixA` |
Whether the factor matrices Ak are updated in each iteration step (Default: FALSE). |

`L1_A` |
Paramter for L1 regularitation (Default: 1e-10). This also works as small positive constant to prevent division by zero, so should be set as 0. |

`L2_A` |
Paramter for L2 regularitation (Default: 1e-10). |

`rank` |
The number of low-dimension in each mode (Default: 3 for each mode). |

`modes` |
The vector of the modes on which to perform the decomposition (Default: 1:K <all modes>). |

`algorithm` |
NTD algorithms. "Frobenius", "KL", "IS", "Pearson", "Hellinger", "Neyman", "HALS", "Alpha", "Beta", "NMF" are available (Default: "Frobenius"). |

`nmf.algorithm` |
NMF algorithms, when the algorithm is "NMF". "Frobenius", "KL", "IS", "Pearson", "Hellinger", "Neyman", "Alpha", "Beta", "ALS", "PGD", "HALS", "GCD", "Projected", "NHR", "DTPP", "Orthogonal", and "OrthReg" are available (Default: "Frobenius"). |

`init` |
The initialization algorithms. "NMF", "ALS", and "Random" are available (Default: "NMF"). |

`Alpha` |
The parameter of Alpha-divergence. |

`Beta` |
The parameter of Beta-divergence. |

`thr` |
When error change rate is lower than thr1, the iteration is terminated (Default: 1E-10). |

`num.iter` |
The number of interation step (Default: 100). |

`num.iter2` |
The number of NMF interation step, when the algorithm is "NMF" (Default: 10). |

`viz` |
If viz == TRUE, internal reconstructed tensor can be visualized. |

`figdir` |
the directory for saving the figure, when viz == TRUE (Default: NULL). |

`verbose` |
If verbose == TRUE, Error change rate is generated in console windos. |

S : K-order tensor object, which is defined as S4 class of rTensor package. A : A list containing K factor matrices. RecError : The reconstruction error between data tensor and reconstructed tensor from S and A. TrainRecError : The reconstruction error calculated by training set (observed values specified by M). TestRecError : The reconstruction error calculated by test set (missing values specified by M). RelChange : The relative change of the error.

Koki Tsuyuzaki

Yong-Deok Kim et. al., (2007). Nonnegative Tucker Decomposition.
*IEEE Conference on Computer Vision and Pattern Recognition*

Yong-Deok Kim et. al., (2008). Nonneegative Tucker Decomposition With
Alpha-Divergence. *IEEE International Conference on Acoustics,
Speech and Signal Processing*

Anh Huy Phan, (2008). Fast and efficient algorithms for nonnegative
Tucker decomposition. *Advances in Neural Networks - ISNN2008*

Anh Hyu Phan et. al. (2011). Extended HALS algorithm for nonnegative
Tucker decomposition and its applications for multiway analysis and classification.
*Neurocomputing*

`plotTensor3D`

```
tensordata <- toyModel(model = "Tucker")
out <- NTD(tensordata, rank=c(2,2,2), algorithm="Frobenius",
init="Random", num.iter=2)
```

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