A Unified Framework for Outlier-Robust PCA-like Algorithms
نویسندگان
چکیده
We propose a unified framework for making a wide range of PCA-like algorithms – including the standard PCA, sparse PCA and non-negative sparse PCA, etc. – robust when facing a constant fraction of arbitrarily corrupted outliers. Our analysis establishes solid performance guarantees of the proposed framework: its estimation error is upper bounded by a term depending on the intrinsic parameters of the data model, the selected PCA-like algorithm and the fraction of outliers. Our experiments on synthetic and realworld datasets demonstrate that the outlier-robust PCA-like algorithms derived from our framework have outstanding performance.
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