2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-34.3
Paper Title ON THE ADVERSARIAL ROBUSTNESS OF PRINCIPAL COMPONENT ANALYSIS
Authors Ying Li, Tongji University, China; Fuwei Li, Lifeng Lai, University of California, Davis, United States; Jun Wu, Fudan University, China
SessionMLSP-34: Subspace Learning and Applications
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-SBML] Subspace and manifold learning
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Abstract In this paper, we investigate the adversarial robustness of principal component analysis (PCA) algorithms. In the considered setup, there is a powerful adversary who can add a carefully designed data point to the original data matrix. The goal of the adversary is to maximize the distance between the subspace learned from the original data and the subspace obtained from the modified data. Different from most of the existing research using Asimov distance to measure such a distance, we leverage a more precise and sophisticated measurement, Chordal distance, which can be used to analyze the influence of an outlier on PCA more comprehensively. Our analysis shows that the first principal angle can be completely changed by an outlier and the second principal angle changes very little. We also demonstrate the performance of our strategy with experimental results on synthetic data and real data.