High-dimensional robust principal component analysis and its applications
Journal of Computational Methods in Sciences and Engineering, ISSN: 1472-7978, Vol: 23, Issue: 5, Page: 2303-2311
2023
- 2Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Principal component analysis method is one of the most widely used statistical procedures for data dimension reduction. The traditional principal component analysis method is sensitive to outliers since it is based on the sample covariance matrix. Meanwhile, the deviation of the principal component analysis based on the Minimum Covariance Determinant (MCD) estimation is significantly increased as the data dimension increases. In this paper, we propose a high-dimensional robust principal component analysis based on the Rocke estimator. Simulation studies and a real data analysis illustrate that the finite sample performance of the proposed method is significantly better than those of the existing methods.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85173257058&origin=inward; http://dx.doi.org/10.3233/jcm-226829; https://journals.sagepub.com/doi/full/10.3233/JCM-226829; https://dx.doi.org/10.3233/jcm-226829; https://content.iospress.com:443/articles/journal-of-computational-methods-in-sciences-and-engineering/jcm226829
SAGE Publications
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