Agarwal, "Subspace-based optimization method for reconstructing perfectly electric conductors," Progress In Electromagnetics Research, Vol. 100, 119-128, 2010.Subspace-based optimization method for reconstructing perfectly electric conductors. X. Ye,X. Chen,Y. Zhong,K. Agarwal. Progress in ...
Numerical simulations validate the fast convergence and robustness of the proposed inverse method.doi:10.1163/156939309789476301ChenX.-DTaylor & Francis GroupJournal of Electromagnetic Waves and ApplicationsChen, X.-D., "Subspace-based optimization method in electric impedance tomography," Journal of ...
We propose a new fast algorithm for solving a TV-based image restoration problem. Our approach is based on merging subspace optimization methods into an augmented Lagrangian method. The proposed algorithm can be seen as a variant of the ALM (Augmented Lagrangian Method), and the convergence ...
Subspace-based optimization method for reconstructing extended scatterers: transverse electric case The subspace-based optimization method is generalized to the transverse electric (TE) case for reconstructing the relative-permittivity profiles of extende... L Pan,K Agarwal,Y Zhong,... - 《Journal of ...
Subspace-based system identification is typically based on an estimate of the extended observability matrix. It is thus of great interest to investigate, and also optimize, the estimate of the observability matrix. Of special interest in this paper is the fact that the influence of certain weightin...
1)Subspace method子空间方法 1.Time-varying system identification using recursive subspace method based on free response data;用于时变系统辨识的自由响应递推子空间方法 2.Detecting network-wide traffic anomalies based on subspace method;基于子空间方法的大规模网络流量异常检测 3.A novel recursive subspace ...
Harmonic Plus Noise Decomposition: Time-Frequency Reassignment Versus a Subspace-Based Method David, B., Emiya, V., Badeau, R., and Grenier, Y. (2006). Harmonic plus noise decomposition : time-frequency reassignment versus a subspace based method. In 120th AES Convention, Paris, France... ...
A new efficient subspace and K-Means clustering based method to improve Collaborative Filtering pythonmachine-learningalgorithmsparsityclusteringsimilaritycollaborative-filteringtree-structurerecommender-systempearsonmovielenssubspacesubspace-clusteringhigh-dimensionalityneighbor-usersnusccf ...
Nuclear norm based subspace identification methods have recently gained importance due to their ability to find low rank solutions while maintaining accuracy through convex optimization. However, their heavy computational burden typically precludes the use in an online, recursive manner, such as may be ...
Based on this intuition, Kang et al. [103], proposed Low-rank Kernel learning for Graph matrix (LKG) that learns a low-rank consensus kernel from a weighted linear combination of the given kernels by solving the following optimization problem: (18)minC,K,w12‖Φ(X)−Φ(X)C‖F2+λ...