Conformal prediction based on principal component analysis for high-dimensional outlier detection

Xiaoyu Qian; Jinru Wu; Youwu Lin

doi:10.1117/12.3050772

27 September 2024 Conformal prediction based on principal component analysis for high-dimensional outlier detection

Xiaoyu Qian, Jinru Wu, Youwu Lin

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 1328106 (2024) https://doi.org/10.1117/12.3050772
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

Current outlier detection methods exhibit many limitations in high-dimensional settings. Traditional statistical approaches rely on strong assumptions and lack practicality and generality. Meanwhile, despite the better performance, machine learning methods suffer from low interpretability and reliability due to their complex mechanisms and the absence of confidence estimation. Although outlier detection methods based on principal component analysis (PCA) have shown some advantages by extracting important features from high-dimensional data, they have not entirely solved these problems. Conformal prediction, a finite-sample distribution-free uncertainty quantification method recently applied to outlier detection, produces a set-valued prediction with a Type-I error guarantee and false discovery rate control. This paper proposes a new distribution-free method for high-dimensional outlier detection, PCA-CP, combining principal component analysis and conformal prediction. PCA-CP overcomes the shortcomings of previous methods, demonstrating high generality and reliability while achieving high performance. Experiments on simulation and real data show that PCA-CP outperforms previous methods, achieving higher power and lower False Discovery Rate, thus proving its significant advantages.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoyu Qian, Jinru Wu, and Youwu Lin "Conformal prediction based on principal component analysis for high-dimensional outlier detection", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 1328106 (27 September 2024); https://doi.org/10.1117/12.3050772

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Principal component analysis

Calibration

Machine learning

Data modeling

Reliability

Matrices

Statistical methods

Show All Keywords

Keywords/Phrases

Search In:

Publication Years