SENSC algorithm is a newly proposed stable and efficient NSC algorithm. In this paper the SENSC algorithm is evaluated for the task of image clustering. A series of experiments are conducted on two different kinds of image datasets, including face images and natural images, and SENSC is compared with some other commonly used clustering methods. Experimental results show that SENSC is better suited for the clustering of non-negative, well structured data which lies in some clear, meaningful underlying low-dimensional subspace.
['Yinfeng Qin', 'Le Li', 'Yu-Jin Zhang']
Evaluation of SENSC Algorithm for Image Clustering