Allerton 2015 Paper Abstract


Paper ThB5.1

Wang, Yining (Carnegie Mellon University), Singh, Aarti (Carnegie Mellon University)

An Empirical Comparison of Sampling Techniques for Matrix Column Subset Selection

Scheduled for presentation during the Regular Session "Sparse Signal Processing" (ThB5), Thursday, October 1, 2015, 10:30−10:50, Lower Level

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Universal Algorithms and Machine Learning


Column subset selection (CSS) is the problem of selecting a small portion of columns from a large data matrix as one form of interpretable data summarization. Leverage score sampling, which enjoys both sound theoretical guarantee and superior empirical performance, is widely recognized as the state-of-the-art algorithm for column subset selection. In this paper, we revisit iterative norm sampling, another sampling based CSS algorithm proposed even before leverage score sampling, and demonstrate its competitive performance under a wide range of experimental settings. We also compare iterative norm sampling with several of its other competitors and show its superior performance in terms of both approximation accuracy and computational efficiency. We conclude that further theoretical investigation and practical consideration should be devoted to iterative norm sampling in column subset selection.



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