A Unified Framework for Outlier-Robust PCA-like Algorithms

author: Wenzhuo Yang, Department of Mechanical Engineering, National University of Singapore
published: Sept. 27, 2015,   recorded: July 2015,   views: 1658
Categories

See Also:

Download slides icon Download slides: icml2015_yang_pca_like_algorithms_01.pdf (1.5 MB)


Help icon Streaming Video Help

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: