1. Coll Control Sci & Engn,China Univ Petr East China
2. China Elect Power Res Inst
3. Coll Sci,China Univ Petr East China
4. Sch Petr Engn,China Univ Petr East China
5. Beijing Adv Innovat Ctr Big Data & Brain Comp,Beihang Univ
6. Ctr Artificial Intelligence & Machine Learning,Indian Stat Inst
折叠
Abstract
Unsupervised feature selection is one of the efficient approaches to reduce the dimension of unlabeled high-dimensional data. We present a novel adaptive autoencoder with redundancy control (AARC) as an unsupervised feature selector. By adding two Group Lasso penalties to the objective function, AARC integrates unsupervised feature selection and determination of a compact network structure into a single framework. Besides, a penalty based on a measure of dependency between features (such as Pearson correlation, mutual information) is added to the objective function for controlling the level of redundancy in the selected features. To realize the desired effects of different regularizers in different phases of the training, we introduce adaptive parameters which change with iterations. In addition, a smoothing function is utilized to approximate the three penalties since they are not differentiable at the origin. An ablation study is carried out to validate the capabilities of redundancy control and structure optimization of AARC. Subsequently, comparisons with nine state-of-the-art methods illustrate the efficiency of AARC for unsupervised feature selection. (c) 2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier