Robust Irregular Tensor Factorization and Completion for Temporal Health Data Analysis

By Yifei Ren, Jian Lou, Li Xiong, and Joyce C. Ho. 2020. Code for ``Robust Irregular TensorFactorization and Completion for Temporal Health Data Analysis". In The 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), 2020.

This repository is designed for a robust PARAFAC2 tensor factorization method for irregular tensors with a new low-rank regularization function to handle potentially missing and erroneous entries in the input tensor.

Before running the codes you need to import Tensor Toolbox Version 2.6 and N-way which can be downloaded from: https://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html

To start, you need to run: "Run_This.m" file. You can select if you want to use the robust version or not: Smooth_COPA: Smooth PARAFAC2 where smoothness apply to U_k factor matrix. Robust_Smooth_COPA : Our robust Repair model.

Here is the lists of Repair functions:

• calculate_fit: -Compute the fit for PARAFAC2 tensor input.

• claculate_norm: -Compute the norm of a PARAFAC2 tensor.

• claculate_norm_observe: -Compute the norm of a PARAFAC2 tensor with error & missing entries.

• MSplineBasis: -This function produce the spline function for subject X_k.

• Robust_Smooth_COPA: Robust Smooth PARAFAC2 where smoothness apply to U_k factor matrix.

• Robust_fastADMM: -Compute the admm for each mode of a tensor.

• Robust_COPA_optimizer -This function is designed to optimize H,W (S_k) and V.

If you find any bug or error in the codes please send an email to: yifei.ren2@emory.edu