Grassmann Averages for Scalable Robust PCA

CVPR 2014 Soren HaubergAasa FeragenMichael J. Black

As the collection of large datasets becomes increasingly automated, the occurrence of outliers will increase -- "big data" implies "big outliers''. While principal component analysis (PCA) is often used to reduce the size of data, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results... (read more)

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