Variational Robust Subspace Clustering with Mean Update Algorithm

Sergej Dogadov, Andrés R. Masegosa, Shinichi Nakajima

ICCVW 2017 pp. 1792-1799

doi:10.1109/ICCVW.2017.212 /iccvw/2017/dogadov2017iccvw-variational/

Abstract

In this paper, we propose an efficient variational Bayesian (VB) solver for a robust variant of low-rank subspace clustering (LRSC). VB learning offers automatic model selection without parameter tuning. However, it is typically performed by local search with update rules derived from conditional conjugacy, and therefore prone to local minima problem. Instead, we use an approximate global solver for LRSC with an element-wise sparse term to make it robust against spiky noise. In experiment, our method (mean update solver for robust LRSC), outperforms the original LRSC, as well as the robust LRSC with the standard VB solver.

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Cite

Text

Dogadov et al. "Variational Robust Subspace Clustering with Mean Update Algorithm." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.212

Markdown

[Dogadov et al. "Variational Robust Subspace Clustering with Mean Update Algorithm." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/dogadov2017iccvw-variational/) doi:10.1109/ICCVW.2017.212

BibTeX

@inproceedings{dogadov2017iccvw-variational,
  title     = {{Variational Robust Subspace Clustering with Mean Update Algorithm}},
  author    = {Dogadov, Sergej and Masegosa, Andrés R. and Nakajima, Shinichi},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {1792-1799},
  doi       = {10.1109/ICCVW.2017.212},
  url       = {https://mlanthology.org/iccvw/2017/dogadov2017iccvw-variational/}
}