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RDFTensor (version 1.3)

Different Tensor Factorization (Decomposition) Techniques for RDF Tensors (Three-Mode-Tensors)

Description

Different Tensor Factorization techniques suitable for RDF Tensors. RDF Tensors are three-mode-tensors, binary tensors and usually very sparse. Currently implemented methods are 'RESCAL' Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel (2012) , 'NMU' Daniel D. Lee and H. Sebastian Seung (1999) , 'ALS', Alternating Least Squares 'parCube' Papalexakis, Evangelos, C. Faloutsos, and N. Sidiropoulos (2012) , 'CP_APR' C. Chi and T. G. Kolda (2012) . The code is mostly converted from MATLAB and Python implementations of these methods. The package also contains functions to get Boolean (Binary) transformation of the real-number-decompositions. These methods also are for general tensors, so with few modifications they can be applied for other types of tensor.

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Version

Install

install.packages('RDFTensor')

Monthly Downloads

14

Version

1.3

License

GPL-3

Maintainer

Abdelmoneim Desouki

Last Published

January 14th, 2021

Functions in RDFTensor (1.3)

default_parcube_options

parCube initialization parameters
CP_01

transformation of the real-number-CP-decompositions to Binary
getTensor

getting tensor from triples
CP_R01

inverse of real-number-CP-decompositions to Binary
cp_nmu

Compute nonnegative CP with multiplicative updates(NMU)
RDFTensor-package

RDFTensor
Tensor_error

RESCAL Tensor error
cp_als

Compute a CP decomposition using an alternating least-squares algorithm(als)
RescalReconstructBack

reconstruct a tensor from RESCAL factorization result
getTensor3m

getting tensor from triples
cp_apr

Compute nonnegative CP with alternating Poisson regression(CP_APR)
rescal_SO_Val

calaculate scores Subj,Predicate,Obj
serial_parCube

Serial 3-mode ParCube algorithm for memory resident tensors
spt_mttkrp

Matricized tensor times Khatri-Rao product for ktensor
tensor2mat

Reduce tensor to matrix
getTnsrijk

tensor frontal slices to indices
scRescal

Scalable RESCAL Factorization
rescal_Trp_Val

RESCAL scores of triples
inv_rescal_sf_prd_chnkgrp

Reconstruct predicate(s) via chunks and grouping
recon_1prd_topn_par

Reconstruct predicate top scores on very large graphs
rescal

RESCAL: Tensor Factorization.
tnsr2trp

sparse tensor to triples
rescal_01

transformation of the real-number-RESCAL-decompositions to Binary
umls_tnsr

RDF tensor of UMLS small graph