Subspace clustering is a widely used clustering method,in which the most critical technology is representation matrix acquisition.To make the representation matrix better fit the block-diagonal structure,this study proposes a subspace clustering method based on an improved trace Lasso norm and the proximal alternating linearized minimization(PALM)algorithm.Firstly,the clean data obtained by subtracting the noise from the original data is used as the dictionary of data self-representation,which makes the representation matrix closer to the block-diagonal structure.Secondly,an improved trace Lasso norm is proposed.It utilizes a non-convex FCP norm to constrain the singular value vector of the matrix,so that the matrix can be better promoted to satisfy the low rank.Finally,due to the non-convexity and non-smoothness of the proposed model and the nonlinearity of the constraint conditions,the PALM algorithm is used to solve the model,which ensures convergence.Numerical experiments of clustering on the CFP face dataset and an animal face image dataset show that the proposed subspace clustering method outperforms the commonly used K-means clustering,spectral clustering,and sparse subspace clustering(SSC).
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