Title: A semi-supervised locally linear embedding spectral clustering algorithm

Authors: Xi Wu; Wangjie Sun

Addresses: School of Biomedical Engineering, Jilin Medical University, Jilin Province, 132013, China ' School of Science, Jilin Institute of Chemical Technology, Jilin Province, 132022, China

Abstract: Many practical problems can be attributed to the clustering problem. Spectral clustering algorithm can be clustered in any shape of space, and obtain the global optimal solution. Based on the classical Ng-Jordan-Weiss (NJW) algorithm, utilising the supervision information to guide the clustering process, the result of clustering is more accurate. Meanwhile, combined the manifold learning with semi-supervised spectral clustering algorithm, and the data dimension will reduce based on locally linear embedding (LLE). Based on the heuristic thinking, calculated distance matrix, a reasonable number of nearest neighbours could be funded, thus we achieve the purpose of dimension reduction. Moreover, clustering based on reduced dimension data, the same clustering results as the original data could be obtained. Experimental results have shown that this algorithm could achieve better clustering effect on artificial datasets and real datasets.

Keywords: locally linear expression; spectral clustering; semi-supervised.

DOI: 10.1504/IJAMC.2017.085935

International Journal of Advanced Media and Communication, 2017 Vol.7 No.2, pp.101 - 112

Received: 04 Jun 2016
Accepted: 13 Sep 2016
Published online: 18 Aug 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article