Abstract: Outlier detection is one of the hot topics in the field of data mining.The existing detection algorithms are mainly applied to the cases where outliers lie in initial attribute subspace or various linear combinations of underlying subspace,when the outliers are embedded in local nonlinear subspace,it is very difficult to detect the outliers effectively.To solve this problem,the shortcomings of typical spectral embedding algorithm for outlier detection were firstly analyzed,and then on the basis of local density,an outlier detection algorithm based on spectral embedding and local density was proposed.The algorithm which uses iterative strategy can efficiently screen unimportant eigenvectors and discover eigenvectors that are relevant for finding outliers hidden in local non-linear subspaces,and the local density-based spectral embedding from a previous iteration is used for improving the similarity graph for the next iteration,such that outliers are gradually segregated from inliers during these iterations.The simulation results show that the detection accuracy of the proposed algorithm is better than other typical algorithms,and it is not sensitive to the parameter setting.

Key words: Detection accuracy, Iterative strategy, Local density, Outlier detection, Similarity graph, Spectral embedding