SSC-TLRR

This is an implementation of our IEEE TCSVT 2023 paper:

Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
Yuheng Jia, Guanxing Lu, Hui Liu, Junhui Hou
Southeast University, Caritas Institute of Higher Education, City University of Hong Kong

This repository contains:

• Datasets and Selected Annotations in our paper, includeing ORL, YaleB, COIL20, Isolet, MNIST, Alphabet, BF0502 and Notting-Hill, and a matched visualization demo.
• A Function to implement the proposed method.
• A Comparision Demo of the mentioned methods (you may need to refer to possible official implementations, or implement them yourself) in our manuscript, including LRR, DPLRR, SSLRR, L-RPCA, CP-SSC, SC-LRR and CLRR.
• Some raw experimental Results.
• A Visualization Demo of the result files.
• A Dataset Visualization Demo to visualize the data.

Usage

Before running the code, you need to download the following toolboxes:

• LibADMM library from: https://github.com/canyilu/LibADMM
• Graph Signal Processing Toolbox (GSPBox) from: https://github.com/epfl-lts2/gspbox
• Clustering Measure from: https://github.com/jyh-learning/MVSC-TLRR

Errata

• We have added genWv3.m, which is used to generate the $k$-NN graph from data.
• We have renamed the function Normalize_test (previously used as a copy of the normalize function for an older version of MATLAB) to normalize for convenience.
• We have added norm21.m to compute the objective value. This does not affect the training progress.

If you still encounter any problems during installation, please feel free to open an issue.

Citation

If you find this repository useful, please consider citing our work:

@ARTICLE{10007868,
  author={Jia, Yuheng and Lu, Guanxing and Liu, Hui and Hou, Junhui},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation}, 
  year={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TCSVT.2023.3234556}}