Linear discriminant analysis guided by unsupervised ensemble learning

• Impact Factor:8.233
• DOI number:10.1016/j.ins.2018.12.036
• Affiliation of Author(s):西南交通大学
• Journal:Information Sciences
• Place of Publication:UNITED STATES
• Abstract:The high dimensionality and sparsity of data often increase the complexity of clustering; these factors occur simultaneously in unsupervised learning. Clustering and linear discriminant analysis (LDA) are methods to reduce the dimensionality and sparsity of data. In this study, the similarity of clustering and LDA are investigated based on their objective functions. Subsequently, their objective functions are integrated, and an LDA guided by an unsupervised ensemble learning (LDA–UEL) model is proposed. To create the proposed model, fuzziness F is designed to measure the confidence of unsupervised learning and the inference of the proposed model is illustrated. Furthermore, a corresponding algorithm for the inference is designed. Finally, extensive experiments are designed, and the results thus obtained demonstrate the effectiveness and high performance of the LDA–UEL model.
• Co-author:Tianrui Li, Shi-Jinn Horng, Xinwen Zhu
• First Author:Ping Deng
• Indexed by:Academic papers
• Correspondence Author:Hongjun Wang
• Document Code:20185206304815
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 480
• Issue:April 2019
• Page Number:Pages 211-221
• ISSN No.:0020-0255
• Translation or Not:no
• Date of Publication:2018-12-21