A Unified Framework for Outlier-Robust PCA-like Algorithms
Abstract
We propose a unified framework for making a wide range of PCA-like algorithms – including the standard PCA, sparse PCA and non-negative sparse PCA, etc. – robust when facing a constant fraction of arbitrarily corrupted outliers. Our theoretic analysis establishes solid performance guarantees of the proposed framework: its estimation error is upper bounded by a term depending on the intrinsic parameters of the data model, the selected PCA-like algorithm and the fraction of outliers. Comprehensive experiments on synthetic and real-world datasets demonstrate that the outlier-robust PCA-like algorithms derived from our framework have outstanding performance.
Cite
Text
Yang and Xu. "A Unified Framework for Outlier-Robust PCA-like Algorithms." International Conference on Machine Learning, 2015.Markdown
[Yang and Xu. "A Unified Framework for Outlier-Robust PCA-like Algorithms." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/yang2015icml-unified/)BibTeX
@inproceedings{yang2015icml-unified,
title = {{A Unified Framework for Outlier-Robust PCA-like Algorithms}},
author = {Yang, Wenzhuo and Xu, Huan},
booktitle = {International Conference on Machine Learning},
year = {2015},
pages = {484-493},
volume = {37},
url = {https://mlanthology.org/icml/2015/yang2015icml-unified/}
}