Self-tuning clustering for high-dimensional data

Abstract

Spectral clustering is an important component of clustering method, via tightly relying on the affinity matrix. However, conventional spectral clustering methods 1). equally treat each data point, so that easily affected by the outliers; 2). are sensitive to the initialization; 3). need to specify the number of cluster. To conquer these problems, we have proposed a novel spectral clustering algorithm, via employing an affinity matrix learning to learn an intrinsic affinity matrix, using the local PCA to resolve the intersections; and further taking advantage of a robust clustering that is insensitive to initialization to automatically generate clusters without an input of number of cluster. Experimental results on both artificial and real high-dimensional datasets have exhibited our proposed method outperforms the clustering methods under comparison in term of four clustering metrics.