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

Summary
Researchers at the National Institute on Drug Abuse (NIDA) seek licensing and/or co-development partners for big data processing by a most efficient robust PCA method.
NIH Reference Number
E-080-2019
Product Type
Keywords
  • Big Data, L1-norm Principal Component Analysis, L1-PCA, Robustness, Outliers, Scalability, Online Flipping, Song
Collaboration Opportunity
This invention is available for licensing and co-development.
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Description of Technology

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

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
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]

Patent Status
  • Research Material: NIH will not pursue patent prosecution for this technology
Updated
Wednesday, May 15, 2019