Traditional spectral clustering algorithms establish direct adjacency matrix using Gaussian kernel

and then do original data clustering

without taking into account deep feature of the data as well as the manifold structure of the neighborhood

but only carry out single cluster

in view of the above three shortcomings

put forward a local spectral clustering and mapping algorithm using sparse autoencoders（LSCMS）. Through data preprocessing

LSCMS uses sparse auto-coding to extract deep characteristics of the original data set

which can better reflect the characteristics of the sample

so as to replace the original data; and reconstructs adjacency matrix by its linear neighborhood instead of Gaussian kernel function. LSCMS clusters and maps data to cluster index simultaneously so as to coordinate the cluster indicator. Experimental results on UCI datasets

handwritten datasets

face datasets show that the algorithm is superior to the existing clustering algorithms.