Research on spectral clustering algorithms based on building different affinity matrix

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.