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In today's big and messy data age, there is a lot of data generated everywhere around us. Examples include texts, tweets, network traffic, changing Facebook connections, or video surveillance feeds coming in from one or multiple cameras. Dimension reduction and noise/outlier removal are usually important preprocessing steps before any high-dimensional (big) data set can be used for inference. A common way to do this is via solving the principal component analysis (PCA) problem or its robustdoi:10.1109/jproc.2018.2853498 fatcat:6d52ecsbgfcnxchfeiugxzoerm