• Corpus ID: 51804306

Robust Principal Component Analysis : Two Analyses in One

@inproceedings{Salis2018RobustPC,
  title={Robust Principal Component Analysis : Two Analyses in One},
  author={Anthony Salis and Zohreh Asgharzadeh and Kyungduck Cha},
  year={2018}
}
Home telematics is a new and growing field in the insurance industry. Whereas traditional rating variables, such as amount of insurance and type of home construction, describe a home by its structure, telematics variables describe how the home is used. Telematics variables are derived from sensors placed in the home, such as thermometers, motion detectors, and smoke detectors. The data from these sensors are summarized in many different ways in order to provide potential model variables. For… 

References

Robust principal component analysis?

TLDR
It is proved that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, this suggests the possibility of a principled approach to robust principal component analysis.