Multi-view Subspace Clustering on Topological Manifold

Authors: Shudong Huang, Hongjie Wu, Yazhou Ren, Ivor Tsang, Zenglin Xu, Wentao Feng, Jiancheng Lv

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experimental results on several benchmark datasets illustrate the effectiveness of the proposed model, compared to the state-of-the-art competitors over the clustering performance.
Researcher Affiliation Collaboration 1College of Computer Science, Sichuan University, China 2School of Computer Science and Engineering, UESTC, China 3Centre for Frontier AI Research, A STAR, Singapore 4School of Computer Science and Technology, Harbin Institute of Technology Shenzhen, China
Pseudocode Yes Algorithm 1: Algorithm to solve Eq. (18) and Algorithm 2: The Algorithm for Eq. (5)
Open Source Code Yes Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We provide them as a URL.
Open Datasets Yes The experiments are conducted on several benchmark datasets, namely, 3Sources, MSRC, 100Leaves, COIL-20, Caltech-7, Caltech-20, and MNIST. The detailed information of all datasets is given in Appendix A. ... We list the datasets and the URLs to download them.
Dataset Splits Yes Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] The important details are provided in Section 4.2 and in the code.
Hardware Specification Yes All experiments are run on a workstation with Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz and 32GB RAM, equipped with an NVIDIA GeForce RTX 2080 Ti GPU.
Software Dependencies Yes All codes are implemented in MATLAB 2021b.
Experiment Setup Yes Here we empirically search them in the range [0.1,0.5,1,5,10,50] for simplicity. ... In general, we could obtain a promising clustering performance by setting α = β = 10 in practical applications.