@inproceedings{d462ae2b63534f28be19d486b16e95bf,
title = "Joint adaptive loss and l2/l0-norm minimization for unsupervised feature selection",
abstract = "Unsupervised feature selection is a useful tool for reducing the complexity and improving the generalization performance of data mining tasks. In this paper, we propose an Adaptive Unsupervised Feature Selection (AUFS) algorithm with explicit l2/l0-norm minimization. We use a joint adaptive loss for data fitting and a l2/l0 minimization for feature selection. We solve the optimization problem with an efficient iterative algorithm and prove that all the expected properties of unsupervised feature selection can be preserved. We also show that the computational complexity and memory use is only linear to the number of instances and square to the number of clusters. Experiments show that our algorithm outperforms the state-of-the-arts on seven different benchmark data sets.",
keywords = "Laplace equations, Optimization",
author = "Mingjie Qian and Chengxiang Zhai",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/IJCNN.2015.7280307",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 International Joint Conference on Neural Networks, IJCNN 2015",
address = "United States",
}