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.