A semi-supervised clustering approach using nonlinear canonical correlation analysis with t-SNE

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298, Xiao, J. and Wei, H. ORCID: https://orcid.org/0000-0002-9664-5748 (2024) A semi-supervised clustering approach using nonlinear canonical correlation analysis with t-SNE. In: 2024 International Joint Conference on Neural Networks (IJCNN), 30 Jun- 5 Jul 2024, Yokohoma, Japan. doi: 10.1109/ijcnn60899.2024.10650841

Abstract/Summary

Clustering of high-dimensional data is a challenging task, since the usual distance measures in high-dimensional space cannot reflect how clusters are partitioned. In this work, by assuming there are some data examples with known labels, a new semi-supervised clustering approach is proposed using a modified canonical correlation analysis and t-SNE. Initially, t-SNE projects high dimensional data onto 3D embedding. While the clusters in the t-SNE embedding space may be visually separable, it is still challenging to achieve very good clustering performance with a conventional unsupervised clustering algorithm. In this work, by using radial basis functions (RBFs) in t-SNE embedding space, centred as some labelled points, a modified canonical correlation analysis algorithm is introduced. The proposed algorithm is referred to as RBF-CCA, which learns the associated projection matrix using supervised learning on the small labelled data set, followed by projection of the associated canonical variables to a large amount of unlabelled data. Then, k-means clustering is applied as the final clustering step. To demonstrate its effectiveness, the proposed algorithm is experimented on several benchmark image data sets.

Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/116055
Item Type	Conference or Workshop Item
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Download/View statistics	View download statistics for this item