Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains (TTC) for Matlab©/Octave©
This package contains Matlab/Octave code for tensor completion using tensor trains with Total Variation Regularization (TTC).
- Functions
- demo
Demonstrates the usage of the TTC algorithm in tensor completions.
- data_generate
Generate the missing entris and known entris for the data.
- b=contract(a)
Sums over all auxiliary indices of a Tensor Network a to return the underlying tensor b.
- y=dotkron(varargin)
Computes the row-wise right-Kronecker product of matrices A,B,C. Note that this computes the right-Kronecker product such that the order of the indices is maintained.
- cores=mpsvd(core,n,r)
Decompose a tensor into its tensor train form with TT-rank r.
- cores=mpsvd_op(core,n,r)
Decompose a tensor into its tensor train form with TT-rank r in an opposite direction.
- TN=tencom(y,u,r,init,varargin)
Tensor completion given the inputs, outputs, TT-ranks, and TT initialization with scalar outputs.
- [TN,e]=tencom_TV(y,u,r,init,Kn,idf,lambda,varargin)
Tensor completion given the inputs, outputs, TT-ranks, TT initialization, coordinate of known entries, dimensions identifier, and TV term parameter lambda with scalar outputs.
- TN=VecOtencom(y,u,r,init,varargin)
Tensor completion given the inputs, outputs, TT-ranks, and TT initialization with vector outputs.
- [TN,e]=VecOtencom_TV(y,u,r,init,Kn,idf,lambda,varargin)
Tensor completion given the inputs, outputs, TT-ranks, TT initialization, coordinate of known entries, dimensions identifier, and TV term parameter lambda with vector outputs.
- Reference
"Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains"
Authors: Ching-Yun Ko, Kim Batselier, Wenjian Yu, Ngai Wong
"Tensor Network alternating linear scheme for MIMO Volterra system identification"
Authors: Kim Batselier, Zhongming Chen, Ngai Wong