NTF: Non-negative CP Decomposition Algorithms (NTF)

Description

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

Usage

NTF(X, rank = 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: 3).

algorithm

NTF algorithms. "Frobenius", "KL", "IS", "Pearson", "Hellinger", "Neyman", "HALS", "Alpha-HALS", "Beta-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

Andrzej CICHOCKI et. al., (2007). Non-negative Tensor Factorization using Alpha and Beta Divergence. IEEE ICASSP 2007

Anh Huy PHAN et. al., (2008). Multi-way Nonnegative Tensor Factorization Using Fast Hierarchical Alternating Least Squares Algorithm (HALS). NOLTA2008

Andrzej CICHOCKI et. al., (2008). Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Examples

Run this code

# NOT RUN {
tensordata <- toyModel(model = "CP")
out <- NTF(tensordata, rank=3, algorithm="Beta-HALS", num.iter=2)
# }