Earticle

초록

영어

Spectral clustering is a clustering method based on algebraic graph theory. It has solid theoretical foundation and good performance of clustering. However, during the process of nonlinear low rank approximation, the traditional spectral clustering algorithm can’t effectively remove redundant features leading to the phenomenon that the local area can not distinguish. It also suffers from the high computational complexity of eigen-decomposition when dealing with the high dimensional data. In order to resolve the aforementioned problems, in this paper a novel Spectral clustering algorithm called LF-SC is proposed. Firstly, based on the nonlinear low dimensional embedding feature selection, we realize dimension reduction. The multi clustering structure of the data is captured, the potential manifold structure is fully discovered, and the geometry structure of the low dimensional manifold clustering is well maintained. Secondly, utilizing the SVD instead of EVD to obtain the eigenvectors reduces the computational complexity and maintain the local structure of the data as well as low dimensional manifold. Extensive experiments show the effectiveness and efficiency of our approach.

목차

Abstract
 1. Introduction
 2. Spectral Clustering based on Nonlinear Low Dimensional Embedding Feature Selection
  2.1 Spectral Clustering
  2.2 Nonlinear low Dimensional Embedding Feature Selection
  2.3 Matrix Factorization
  2.4 Obtaining the Eigenvectors by Singular Value Decomposition
 3. Experiment and Analysis
 4. Conclusions
 Acknowledgement
 References

키워드

저자

• Daowen Zhang [ Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China ]
• Zhiping Zhou [ Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Wuxi 214122, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605