FMR (Flexible Multi-View Representation Learning for Subspace Clustering)

This is the Matlab implementation of Flexible Multi-View Representation Learning for Subspace Clustering, published in IJCAI 2019.

Contact: Ruihuang Li ([email protected])

Paper

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.

Example Results

Data

In this example, we load Yale dataset with 165 grayscale face images of 15 subjects.

Run from

demo_FMR.m

Cite

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},
}