Provable variable selection for streaming features

Jing Wang, Jie Shen, Ping Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In large-scale machine learning applications and high-dimensional statistics, it is ubiquitous to address a considerable number of features among which many are redundant. As a remedy, on-. line feature selection has attracted increasing attention in recent years. It sequentially reveals features and evaluates the importance of them. Though online feature selection has proven an elegant methodology, it is usually challenging to carry out a rigorous theoretical characterization. In this work, we propose a provable online feature selection algorithm that utilizes the online leverage score. The selected features are then fed to κ-means clustering, making the clustering step memory and computationally efficient. We prove that with high probability, performing κ-means clustering based on the selected feature space does not deviate far from the optimal clustering using the original data. The empirical results on realworld data sets demonstrate the effectiveness of our algorithm.

Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
Pages8220-8228
Number of pages9
ISBN (Electronic)9781510867963
StatePublished - 2018
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume12

Conference

Conference35th International Conference on Machine Learning, ICML 2018
Country/TerritorySweden
CityStockholm
Period10/07/1815/07/18

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