Scalable Normalized Cut with Improved Spectral Rotation

Scalable Normalized Cut with Improved Spectral Rotation

Xiaojun Chen, Feiping Nie, Joshua Zhexue Huang, Min Yang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 1518-1524. https://doi.org/10.24963/ijcai.2017/210

Many spectral clustering algorithms have been proposed and successfully applied to many high-dimensional applications. However, there are still two problems that need to be solved: 1) existing methods for obtaining the final clustering assignments may deviate from the true discrete solution, and 2) most of these methods usually have very high computational complexity. In this paper, we propose a Scalable Normalized Cut method for clustering of large scale data. In the new method, an efficient method is used to construct a small representation matrix and then clustering is performed on the representation matrix. In the clustering process, an improved spectral rotation method is proposed to obtain the solution of the final clustering assignments. A series of experimental were conducted on 14 benchmark data sets and the experimental results show the superior performance of the new method.
Keywords:
Machine Learning: Machine Learning
Machine Learning: Unsupervised Learning