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A Most Efficient and Convergent Principal Component Analysis (PCA) Method for Big Data
Abstract: 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...
Published: 9/9/2024   |   Inventor(s): Xiaowei Song
Keywords(s): Big Data, L1-norm Principal Component Analysis, L1-PCA, Online Flipping, Outliers, Robustness, Scalability, Song
Category(s): Collaboration Sought > Collaboration, Application > Research Materials, Collaboration Sought > Licensing