Unsupervised feature selection via joint local learning and group sparse regression
Autor: | Yue-qing Zhang, Can Wang, Yue Wu, Jia-jun Bu |
---|---|
Rok vydání: | 2019 |
Předmět: |
Computer Networks and Communications
business.industry Group (mathematics) Computer science Feature selection Pattern recognition 02 engineering and technology Regression Hardware and Architecture 020204 information systems Signal Processing Web page 0202 electrical engineering electronic engineering information engineering Key (cryptography) Feature (machine learning) 020201 artificial intelligence & image processing Noise (video) Artificial intelligence Electrical and Electronic Engineering business Cluster analysis |
Zdroj: | Frontiers of Information Technology & Electronic Engineering. 20:538-553 |
ISSN: | 2095-9230 2095-9184 |
Popis: | Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However, due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression (JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets (images, voices, and web pages) demonstrate the effectiveness of JLLGSR. |
Databáze: | OpenAIRE |
Externí odkaz: |