DocumentCode :
620261
Title :
Research on spectral clustering algorithms based on building different affinity matrix
Author :
Xu Degang ; Zhao Panlei ; Gui Weihua ; Yang Chunhua ; Xie Yongfang
Author_Institution :
Coll. of Inf. Sci. & Eng., Central South Univ., Changsha, China
fYear :
2013
fDate :
25-27 May 2013
Firstpage :
3160
Lastpage :
3165
Abstract :
As one of the most popular researches in the field of machine learning, spectral clustering algorithms have made great process in many different applications such as image processing. However, there are still some unsolved problems about spectral clustering algorithms, which should be immediately dealt with .These problems include how to build the affinity matrix, and how to deal with the eigenvectors. This paper mainly focuses on building the affinity matrix, which is the most important problem of spectral clustering algorithms. We propose four different methods to build the affinity matrix including the Gaussian kernel function, the Minkowski function, the nearest-correlation function and the local scale function. Then, we develop four new algorithms to contrast the clustering results. Finally, we find that building appropriate local scale function is the most available method to formulate the affinity matrix for spectral clustering algorithm.
Keywords :
Gaussian processes; eigenvalues and eigenfunctions; matrix algebra; pattern clustering; Gaussian kernel function; Minkowski function; affinity matrix; eigenvectors; local scale function; machine learning; nearest-correlation function; spectral clustering algorithms; Algorithm design and analysis; Buildings; Clustering algorithms; Correlation; Kernel; Machine learning algorithms; Vectors; Gaussian kernel function; Local scale function; Minkowski function; Nearest-correlation function; Spectral clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
Type :
conf
DOI :
10.1109/CCDC.2013.6561490
Filename :
6561490
Link To Document :
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