mty9678 / Efficient_STTM

This repository contains MATLAB files for the implementation of work proposed in the paper Efficient Structure-preserving Support Tensor Train Machine.

Efficient Structure-preserving Support Tensor Train Machine

This repository contains MATLAB files for the implementation of work proposed in the paper Efficient Structure-preserving Support Tensor Train Machine.

Intro

The key novelty of our research is a stable and well explained Support Vector Machine (SVM) model for low-rank tensor input data that manifests much higher classification accuracy and banchmarked compared to other state-of-the-art methods. Our paper presents a general SVM framework using the Tensor-Train decomposition along with the explanation, validation and importance of each stage of the proposed algorithm with a graphical illustration.

Dataset

Folder- dataset

• ADNI_first - fMRI dataset for Alzheimer disease

• ADHD - fMRI dataset for Attention Deficit Hyperactivity Disorder

Setup

Addpath

1. Tensor Train Toolbox
2. LIBSVM

Functions and Results

Each folder presents results for each step of algorithm, presented in paper.

• Folder - TT_KTT_code (Result_TT_KTT.m)

• Folder - TT_UoSVD_KTT_code (Result_TT_UoSVD_KTT.m)

• Folder - TT-CP_UoSVD_KTT_code (final_results_TTCP_UoSVD_KTT.m)

• Folder - TTCP_MMK_code (Mainfile_results.m)

Comparision of our method to state-of-the-art

Cite As

If you use our work and codes for the further research then please cite the paper [Efficient_STTM].

BibTeX

@misc{kour2020efficient,
      title={Efficient Structure-preserving Support Tensor Train Machine}, 
      author={Kirandeep Kour and Sergey Dolgov and Martin Stoll and Peter Benner},
      year={2020},
      eprint={2002.05079},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

If you have any query/suggestion, kindly write to Kirandeep Kour at [e-mail].