Incremental relevance sample-feature machine: A fast marginal likelihood maximization approach for joint feature selection and classification

作者：

Highlights：

• We developed a joint feature selection and classification method with constructive structure which can be used practically for large data sets.

• The proposed method can produce state-of-the-art performance in terms of accuracy.

• The proposed method benefits from sparser learned model both in sample and feature domains.

• The run-time of the proposed method is much less than the state-of-the-art algorithms especially for large data sets.

摘要

Highlights•We developed a joint feature selection and classification method with constructive structure which can be used practically for large data sets.•The proposed method can produce state-of-the-art performance in terms of accuracy.•The proposed method benefits from sparser learned model both in sample and feature domains.•The run-time of the proposed method is much less than the state-of-the-art algorithms especially for large data sets.

论文关键词：Sparse Bayesian learning,Bayesian inference,Embedded feature selection methods,Relevance sample-feature machine (RSFM),Fast marginal likelihood maximization

论文评审过程：Received 31 July 2015, Revised 26 June 2016, Accepted 28 June 2016, Available online 1 July 2016, Version of Record 22 July 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.06.028