Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling

1 Jul 2014Xingguo LiJarvis Haupt

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance; our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix -- as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors... (read more)

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