@inproceedings{ce01b2300185402f98e1b2e4f67e46c6,
title = "R1-PCA: Rotational invariant L1-norm principal component analysis for robust subspace factorization",
abstract = "Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant Li-norm PCA (R1-PCA). R1-PCA is similar to PCA in that (1) it has a unique global solution, (2) the solution are principal eigenvectors of a robust covariance matrix (re-weighted to soften the effects of outliers), (3) the solution is rotational invariant. These properties are not shared by the L1-norm PCA. A new subspace iteration algorithm is given to compute R1-PCA efficiently. Experiments on several real-life datasets show R1-PCA can effectively handle outliers. We extend R1-norm to K-means clustering and show that L1-norm K-means leads to poor results while R 1-K-means outperforms standard K-means.",
author = "Chris Ding and Ding Zhou and Xiaofeng He and Hongyuan Zha",
year = "2006",
doi = "10.1145/1143844.1143880",
language = "英语",
isbn = "1595933832",
series = "ACM International Conference Proceeding Series",
pages = "281--288",
booktitle = "ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006",
note = "23rd International Conference on Machine Learning, ICML 2006 ; Conference date: 25-06-2006 Through 29-06-2006",
}