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Mar, 2017
弱次模可扩展的贪心特征选择
Scalable Greedy Feature Selection via Weak Submodularity
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Rajiv Khanna, Ethan Elenberg, Alexandros G. Dimakis, Sahand Negahban, Joydeep Ghosh
TL;DR
针对大型数据集中贪心算法的运行时间会非常高的问题,本文介绍了2种运用分布式计算和随机评估技术的更快逼近贪心向前选择算法,并且证明了弱次模性的泛化概念足以为这2种算法提供乘性逼近保证。同时,研究者还表明这些快速贪心逼近算法在人工数据和真实数据集上的性能要优于多个已有的基线算法。
Abstract
greedy algorithms
are widely used for problems in
machine learning
such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of even
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