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- W2087445541 abstract "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. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance, and also investigate extensions to settings where the data are noisy, or possibly incomplete." @default.
- W2087445541 created "2016-06-24" @default.
- W2087445541 creator A5011305900 @default.
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- W2087445541 date "2015-04-01" @default.
- W2087445541 modified "2023-10-18" @default.
- W2087445541 title "Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling" @default.
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- W2087445541 doi "https://doi.org/10.1109/tsp.2015.2401536" @default.
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