Code supplement for "Optimal Shrinkage of Singular Values Under Random Data Contamination"
Abstract/Contents
- Abstract
- A low rank matrix X has been contaminated by uniformly distributed noise, missing values, outliers and corrupt entries. Reconstruction of X from the singular values and singular vectors of the contaminated matrix Y is a key problem in machine learning, computer vision and data science. In this paper we show that common contamination models (including arbitrary combinations of uniform noise, missing values, outliers and corrupt entries) can be described efficiently using a single framework. We develop an asymptotically optimal algorithm that estimates X by manipulation of the singular values of Y , which applies to any of the contamination models considered. Finally, we find an explicit signal-to-noise cutoff, below which estimation of X from the singular value decomposition of Y must fail, in a well- defined sense.
Description
| Type of resource | software, multimedia |
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| Date created | October 2017 |
Creators/Contributors
| Author | Barash, Danny | |
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| Author | Gavish, Matan |
Subjects
| Subject | singular value shrinkage |
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| Subject | data contamination |
Bibliographic information
| Related Publication | Barash, D. and Gavish, M. (2017). Optimal shrinkage of singular values under random data contamination. Advances in Neural Information Processing Systems 30 (NIPS 2017). https://papers.nips.cc/paper/7196-optimal-shrinkage-of-singular-values-under-random-data-contamination |
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| Location |
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| Location | https://purl.stanford.edu/kp113fq0838 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred Citation
- Danny Barash and Matan Gavish. (2017). Code supplement for "Optimal Shrinkage of Singular Values Under Random Data Contamination". Stanford Digital Repository. Availalbe at https://purl.stanford.edu/kp113fq0838
Collection
Stanford Research Data
View other items in this collection in SearchWorksContact information
- Contact
- gavish@stanford.edu
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