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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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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
Basic (Target Identification)
Rosenberg SA, et al. Adoptive cell transfer as personalized immunotherapy for human cancer.
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
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