NTD: Non-negative Tucker Decomposition Algorithms (NTD)

Description

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).

Usage

NTD(X, rank = c(3, 3, 3), modes = 1:3, algorithm = "KL", init = "NMF", Alpha = 1,
  Beta = 2, thr = 1e-10, num.iter = 100, viz = FALSE,
  figdir = NULL, verbose = FALSE)

Arguments

The input tensor which has I1, I2, and I3 dimensions.

rank

The number of low-dimension in each mode (J1, J2, J3, J1<I1, J2<I2, J3 < I3) (Default: c(3,3,3)).

modes

The vector of the modes on whih to perform the decomposition (Default: 1:3 <all modes>).

algorithm

NTD algorithms. "Frobenius", "KL", "IS", "Pearson", "Hellinger", "Neyman", "HALS", "Alpha", and "Beta" 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).

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.

Value

S : Tensor object, which is defined as S4 class of rTensor package. A : A list containing three factor matrices. RecError : The reconstruction error between data tensor and reconstructed tensor from S and A RelChange : The relative change of the error

References

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

Examples

Run this code

# NOT RUN {
tensordata <- toyModel(model = "Tucker")
out <- NTD(tensordata, rank=c(2,2,2), algorithm="Frobenius", init="Random", num.iter=2)
# }

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