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.

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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

Commercial Applications:

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