Abstract

Feature selection has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. Since real-world data is usually unlabeled, unsupervised feature selection has received increasing attention in recent years. Without label information, unsupervised feature selection needs alternative criteria to define feature relevance. Recently, data reconstruction error emerged as a new criterion for unsupervised feature selection, which defines feature relevance as the capability of features to approximate original data via a reconstruction function. Most existing algorithms in this family assume predefined, linear reconstruction functions. However, the reconstruction function should be data dependent and may not always be linear especially when the original data is high-dimensional. In this paper, we investigate how to learn the reconstruction function from the data automatically for unsupervised feature selection, and propose a novel reconstruction-based unsupervised feature selection framework REFS, which embeds the reconstruction function learning process into feature selection. Experiments on various types of realworld datasets demonstrate the effectiveness of the proposed framework REFS.

Original languageEnglish (US)
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2159-2165
Number of pages7
ISBN (Electronic)9780999241103
StatePublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: Aug 19 2017Aug 25 2017

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period8/19/178/25/17

Fingerprint

Feature extraction
Data mining
Learning systems
Labels
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Li, J., Tang, J., & Liu, H. (2017). Reconstruction-based unsupervised feature selection: An embedded approach. In 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 2159-2165). International Joint Conferences on Artificial Intelligence.

Reconstruction-based unsupervised feature selection : An embedded approach. / Li, Jundong; Tang, Jiliang; Liu, Huan.

26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, 2017. p. 2159-2165.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, J, Tang, J & Liu, H 2017, Reconstruction-based unsupervised feature selection: An embedded approach. in 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, pp. 2159-2165, 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 8/19/17.
Li J, Tang J, Liu H. Reconstruction-based unsupervised feature selection: An embedded approach. In 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence. 2017. p. 2159-2165
Li, Jundong ; Tang, Jiliang ; Liu, Huan. / Reconstruction-based unsupervised feature selection : An embedded approach. 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, 2017. pp. 2159-2165
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