This is the Matlab implementation of Flexible Multi-View Representation Learning for Subspace Clustering, published in IJCAI 2019.
Contact: Ruihuang Li (liruihuang@tju.edu.cn)
The main contributions include:
- We propose to construct a latent representation by encouraging it to be similar to different views in a weighted way, which implicitly enforces it to encode complementary information from multiple views.
- We introduce the kernel dependence measure: Hilbert Schmidt Independence Criterion (HSIC), to capture high-order, non-linear relationships among different views, which benefits recovering underlying cluster structure of data.
In this example, we load Yale dataset with 165 grayscale face images of 15 subjects.
demo_FMR.m
Please cite following papers if you use this code in your own work:
@inproceedings{li2019flexible,
title={Flexible multi-view representation learning for subspace clustering},
author={Li, Ruihuang and Zhang, Changqing and Hu, Qinghua and Zhu, Pengfei and Wang, Zheng},
booktitle={Proceedings of the 28th International Joint Conference on Artificial Intelligence},
pages={2916--2922},
year={2019},
}