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Column Selection via Adaptive Sampling
Saurabh Paul · Malik Magdon-Ismail · Petros Drineas

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #76

Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm. Our algorithm delivers a tighter theoretical bound on the approximation error which we also demonstrate empirically using two well known relative-error column subset selection algorithms. Our experimental results on synthetic and real-world data show that our algorithm outperforms non-adaptive sampling as well as prior adaptive sampling approaches.

Author Information

Saurabh Paul (Paypal Inc)
Malik Magdon-Ismail (RPI)
Petros Drineas (Rensselaer Polytechnic Institute)

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