Xinzhong Zhu

TL;DR: Wang et al. as discussed by the authors integrated imputation and clustering into a unified learning procedure, which does not require that there is at least one complete base kernel matrix over all the samples.

TL;DR: This work proposes Late Fusion Incomplete Multi-view Clustering (LF-IMVC) which effectively and efficiently integrates the incomplete clustering matrices generated by incomplete views and develops a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence.

TL;DR: A low-rank representation model is employed to learn a shared sample representation coefficient matrix to generate the affinity graph and diversity regularization is used to learn the optimal weights for each view, which can suppress the redundancy and enhance the diversity among different feature views.

TL;DR: This paper designs an unsupervised linear feature selective projection (FSP) for feature extraction with low-rank embedding and dual Laplacian regularization, with the aim to exploit the intrinsic relationship among data and suppress the impact of noise.

TL;DR: The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by capturing the underlying manifold geometry structure of original data points, and leveraging the complementary information among multiple graphs.