Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation. Issue 8 (21st September 2021)

• Record Type: Journal Article
• Authors: Zhao, Liang; Yang, Tao; Zhang, Jie; Chen, Zhikui
• Other Names: Wang Qi guestEditor.; Yu Hongkai guestEditor.; Wang Song guestEditor.; Lin Jianzhe guestEditor.
• Is Part Of: IET computer vision. Volume 15:Issue 8(2021)
• Page Start: 573
• Page End: 591
• Publication Date: 2021-09-21
• Subjects: correlated feature learning -- data clustering -- incremental feature learning -- multi‐view data -- unsupervised learning; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/cvi2.12067
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract: In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi‐view data becomes a challenging problem. Some single‐view methods focus on processing the data dynamically, but they are not suitable for multi‐view data. Some online multi‐view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi‐view Correlated Feature Learning (IMCFL) based on non‐negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi‐view common feature learning. Extensive experiments on real‐world datasets confirm its superiority by comparing it with other state‐of‐the‐art incremental and non‐incremental methods.