A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)

Authors

  • Baokun He The University of Texas at Dallas
  • Guihong Wan The University of Texas at Dallas
  • Haim Schweitzer The University of Texas at Dallas

DOI:

https://doi.org/10.1609/aaai.v34i10.7175

Abstract

Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a “bias trick” that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.

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Published

2020-04-03

How to Cite

He, B., Wan, G., & Schweitzer, H. (2020). A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13807-13808. https://doi.org/10.1609/aaai.v34i10.7175

Issue

Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks

Section

Student Abstract Track