Abstract
This chapter studies the subset selection problem under multiplicative and additive noise. We disclose that the greedy algorithm and POSS algorithms achieve nearly the same approximation guarantee under noise. Moreover, the PONSS algorithm using a noise handling strategy can achieve a better approximation ratio for independently and identically distributed noise. Empirical results on influence maximization and sparse regression verify the advantage of PONSS.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Subset Selection: Noise. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_17
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DOI: https://doi.org/10.1007/978-981-13-5956-9_17
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5955-2
Online ISBN: 978-981-13-5956-9
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