Miaomiao Li

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: This paper proposes an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm, which proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix to address issues of intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance.

TL;DR: A simple yet effective neighbor-kernel-based MKC algorithm that back-projects the solution of the unconstrained counterpart to its principal components and reveals an interesting insight into the exact-rank constraint in ridge regression by careful theoretical analysis.

TL;DR: A consensus learning guided multi-view unsupervised feature selection method, which embeds multi-View feature selection into a non-negative matrix factorization based clustering with sparse constrain.

TL;DR: A novel K-means based clustering algorithm is proposed which unifies the clustering and imputation into one single objective function and makes these two processes be negotiable with each other to achieve optimality.