  We present a general-purpose data compression algorithm, Regularized L21
Semi-NonNegative Matrix Factorization (L21 SNF). L21 SNF provides robust,
parts-based compression applicable to mixed-sign data for which high fidelity,
individualdata point reconstruction is paramount. We derive a rigorous proof of
convergenceof our algorithm. Through experiments, we show the use-case
advantages presentedby L21 SNF, including application to the compression of
highly overdeterminedsystems encountered broadly across many general machine
learning processes.
