A Most Efficient and Convergent Principal Component Analysis (PCA) Method for Big Data


Big data usually means big sample size with many outliers, in which traditional scalable L2-norm principal component analysis (L2-PCA) will fail. Current existing L1-norm PCA (L1-PCA) methods can improve robustness over outliers, however, its scalability is usually limited in either sample size or dimension size.  The inventor proposes an online flipping method to solve L1-PCA challenges, which is not only convergent asymptotically (or with big data), but also achieves most efficiency in the sense each sample is visited only once to extract one principal component (PC). The proposed PCA also has certain robustness to outliers compared to L2-PCA.

If you need a linear complexity robust PCA solver, please contact us; our method can even solve robust PCA in real-time. This efficient robust PCA algorithm is available for licensing and/or collaborations to explore utility for your application.



Potential Commercial Applications: Competitive Advantages:
  • Big data analysis 
  • This approach may be the indicated procedure in the presence of unbalanced outlier contamination
 

Current existing L1-norm PCA (L1-PCA) methods can improve robustness over outliers, however, its scalability is usually limited in either sample size or dimension size. The proposed PCA also has certain robustness to outliers compared to L2-PCA



Development Stage:
Basic (Target Identification)

Inventors:

Xiaowei Song (NIDA)  ➽ more inventions...


Intellectual Property:

Publications:
Song X, An intuitive and most efficient L1-norm principal component analysis algorithm for big data, the 53rd conference on information sciences and systems, 2019  IEEE abstract

Collaboration Opportunity:

Licensing and research collaboration


Licensing Contact:
John Hewes, Ph.D.
Email: John.Hewes@nih.gov
Phone: 240-276-5515

OTT Reference No: E-080-2019
Updated: May 15, 2019