parafac

[IEEE TKDE 2023] A list of up-to-date papers on streaming tensor decomposition, tensor tracking, dynamic tensor analysis

PyTorch implementation of Robust Non-negative Tensor Factorization appearing in N. Dey, et al., "Robust Non-negative Tensor Factorization, Diffeomorphic Motion Correction and Functional Statistics to Understand Fixation in Fluorescence Microscopy".

Collection of basic and advanced Tensor Algebra operations using Matlab and Python.

Canonical Decomposition of Data-Independent-Acquired Spectra

Coupled matrix-tensor factorization for integrating EEG and ffMRI on the brain cortical surface with source reconstruction

R package SDA4D for performing 4-dimensional sparse Bayesian tensor decomposition

PCA for UV-VIS Spectroscopy, PARAFAC for spectrofluorimetry

Implements the code from the publication A distributed proximal gradient descent methods for tensor completion

Weighted orthogonal non-negative (WON) parallel factor analsyis (PARAFAC)

Factorization techniques for the analysis and separation of fetal ECG from maternal ECG for MSc Thesis in Numerical Methods for Computer Science @ Università degli Studi di Bari Aldo Moro, AY 2024/25

Classification with Parallel Factor Analysis

Java library of multi-way algorithms.

8. Latentna struktura PCA, sparse PCA, ICA, NMF, FA (factor analysis), SVD, truncated SVD, CCA, PLS (kao veza blokova), CP/PARAFAC, Tucker, tensor train, LDA/topic models, LSA, embedding struktura (samo kao mapa sličnosti)

Application of the R package cpfa to real datasets.

Interactive MATLAB interface for masking Rayleigh and Raman scatter in EEM fluorescence data and handling masked pixels with NaN, interpolation, SVD, PARAFAC-ALS, O-ALS, or MCR-ALS.

Kernelize transforms bilinear data (exponential decays) using kernels to provide trilinear data.

The PARAFAC-ALS (Lite version) algorithm unmixes trilinear data to obtain the pure components.

Complete scripts used to produce the results of Imperial MRes Biomedical Research - Data Science 2024-2025 Project 1.