ICML 2015poster9 citations
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.
BibTeX
@InProceedings{pmlr-v37-yangc15,
title = {A Unified Framework for Outlier-Robust PCA-like Algorithms},
author = {Yang, Wenzhuo and Xu, Huan},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {484--493},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/yangc15.pdf},
url = {https://proceedings.mlr.press/v37/yangc15.html},
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.}
}