A semi-supervised locally linear embedding spectral clustering algorithm

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.