Unsupervised Ensemble Learning Improves Discriminability of Stochastic Neighbor Embedding

• DOI number:10.1007/s44196-023-00203-y
• Affiliation of Author(s):西南交通大学
• Journal:International Journal of Computational Intelligence Systems
• Place of Publication:FRANCE
• Key Words:Stochastic neighbor embedding; Feature learning; Discriminant learning; Clustering ensemble; Pairwise constraints
• Abstract:The purpose of feature learning is to obtain effective representation of the raw data and then improve the performance of machine learning algorithms such as clustering or classification. Some of the existing feature learning algorithms use discriminant information in the data to improve the representation of data features, but the discrimination of the data feature representation is not enough. In order to further enhance the discrimination, discriminant feature learning based on t-distribution stochastic neighbor embedding guided by pairwise constraints (pcDTSNE) is proposed in this paper. pcDTSNE introduces pairwise constraints by clustering ensemble and uses these pairwise constraints to impose penalties on the objective function, which makes sample points in the mapping space present stronger discrimination. In order to verify the feature learning performance of pcDTSNE, extensive experiments are carried out on several public data sets. The experimental results show that the expression ability of data representation generated by pcDTSNE is further improved.
• Co-author:Hui Zhao,Yinghui Zhang,Jin Guo
• First Author:Jian Wang
• Indexed by:SCI
• Correspondence Author:Hongjun Wang
• Discipline:Engineering
• Document Type:J
• Volume:16
• Issue:1
• Page Number:29
• ISSN No.:1875-6883
• Translation or Not:no
• Date of Publication:2023-03-10
• Included Journals:SCI