The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same sampl...The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.展开更多
Empirical functional models for the maximum and minimum detectable deformation gradient of PALSAR interferometry were established based on coherence and discrete look numbers. Then, a least square regression method wa...Empirical functional models for the maximum and minimum detectable deformation gradient of PALSAR interferometry were established based on coherence and discrete look numbers. Then, a least square regression method was used to fit the model coefficients and thus obtain the generalized functional models for both coherence and look numbers. The experimental results with ALOS PALSAR data of Wenchuan earthquake of China show that the new model works well for judging whether the deformation gradient can be detected by the D-InSAR technology or not. The results can help researchers to choose PALSAR data and to configure processing parameters, and also benefit the interpretation of the measured surface deformation.展开更多
基金supported by National Natural Science Foundation of China(No.61806006)Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.
基金Projects(41222227,U1231105)supported by the National Natural Science Foundation of ChinaProject(13JJ1006)supported by the Natural Science Foundation of Hunan Province,China
文摘Empirical functional models for the maximum and minimum detectable deformation gradient of PALSAR interferometry were established based on coherence and discrete look numbers. Then, a least square regression method was used to fit the model coefficients and thus obtain the generalized functional models for both coherence and look numbers. The experimental results with ALOS PALSAR data of Wenchuan earthquake of China show that the new model works well for judging whether the deformation gradient can be detected by the D-InSAR technology or not. The results can help researchers to choose PALSAR data and to configure processing parameters, and also benefit the interpretation of the measured surface deformation.
