Abstract: The processing of high-dimensional data sets is the focus of computer vision.Subspace clustering is one of the most widely used methods to achieve high-dimensional data clustering.The traditional subspace clustering assumes that the data comes from different linear subspaces,and different subspace regions do not overlap.However,real data often do not meet these two constraints,which affects the effect of subspace clustering.In order to deal with these two problems,this paper introduces a kernelized subspace to solve the nonlinear problem of subspace data,and introduces the second-order neighbors of the subspace coefficient matrix to deal with the overlapping subspace problem.Then a three-step clustering algorithm based on second-order neighbors of the kernelized subspace is designed.Firstly,the self-similarity coefficients of the kernelized subspace data are obtained.Secondly,the overlapping regions of the subspaces are eliminated.Finally,the coefficient matrix is spectrally clustered.In this paper,the designed subspace clustering algorithm is first tested on three artificial data sets,and then the experiment is performed on 12 real data sets,including face,scene characters and biomedical data sets.Experimental results show that the proposed algorithm has certain advantages over the latest algorithms.

Key words: Alternating direction multiplier method, Image identification, Kernel method, Second-order neighbors, Subspace clustering