L1-norm Principal-Component Analysis (L1-PCA) is known to attain remarkable resis tance against faulty/corrupted points among the processed data. However, computing L1 PCA of "big data" with large number of measurements and/or dimensions may be com putationally impractical. This work proposes new algorithmic solutions for incremental and adaptive L1-PCA. The ?rst algorithm computes L1-PCA incrementally, processing one measurement at a time, with very low computational and memory requirements; thus, it is appropriate for big data and big streaming data applications. The second algorithm combines the merits of the ?rst one with additional ability to track changes in the nominal signal subspace by revising the computed L1-PCA as new measurements arrive, demon strating both robustness against outliers and adaptivity to signal-subspace changes. The proposed algorithms are evaluated in an array of experimental studies on subspace esti mation, video surveillance (foreground/background separation), image conditioning, and direction-of-arrival (DoA) estimation.
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