Unsupervised feature selection by pareto optimization

Feng，Chao1; Qian，Chao1; Tang，Ke2

2019

33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

3534-3541

Dimensionality reduction is often employed to deal with the data with a huge number of features, which can be generally divided into two categories: feature transformation and feature selection. Due to the interpretability, the efficiency during inference and the abundance of unlabeled data, unsupervised feature selection has attracted much attention. In this paper, we consider its natural formulation, column subset selection (CSS), which is to minimize the reconstruction error of a data matrix by selecting a subset of features. We propose an anytime randomized iterative approach POCSS, which minimizes the reconstruction error and the number of selected features simultaneously. Its approximation guarantee is well bounded. Empirical results exhibit the superior performance of POCSS over the state-of-the-art algorithms.

其他

英语

EI

20203509102159

Iterative methods ; Metadata ; Multiobjective optimization ; Pareto principle

Optimization Techniques:921.5 ; Numerical Methods:921.6

2-s2.0-85089873631

Scopus

1.Anhui Province Key Lab of Big Data Analysis and Application,School of Computer Science and Technology,University of Science and Technology of China,Hefei,230027,China
2.Shenzhen Key Lab of Computational Intelligence,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Feng，Chao,Qian，Chao,Tang，Ke. Unsupervised feature selection by pareto optimization[C],2019:3534-3541.