A New Robust Subspace Recovery Algorithm (Student Abstract)

Authors

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

Keywords:

Robust Subspace Recovery, Robust Principal Component Analysis, Outlier Detection, Dimensionality Reduction, Rank-one Modification

Abstract

A common task in data analysis is to compute an approximate embedding of the data in a low dimensional subspace. This is used, for example, for dimensionality reduction. Robust Subspace Recovery computes the embedding by ignoring a fraction of the data considered as outliers. Its performance can be evaluated by how accurate the inliers (non-outliers) are represented. We propose a new algorithm that outperforms the current state of the art when the data is dominated by outliers. The main idea is to rank each point by evaluating the change in the global PCA error when that point is considered as an outlier. We show that this lookahead procedure can be implemented efficiently by centered rank-one modifications.

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Published

2021-05-18

How to Cite

Wan, G., & Schweitzer, H. (2021). A New Robust Subspace Recovery Algorithm (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15911-15912. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17952

Issue

Section

AAAI Student Abstract and Poster Program