Jianping Yin

TL;DR: The Improved Deep Embedded Clustering (IDEC) algorithm is proposed, which manipulates feature space to scatter data points using a clustering loss as guidance and can jointly optimize cluster labels assignment and learn features that are suitable for clustering with local structure preservation.

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: This paper theoretically uncovers the connection between existing k-means clustering and the alignment between base partitions and consensus partition and proposes a simple but effective multi-view algorithm termed MVC-LFA, which proposes to maximally align the consensus partition with the weighted base partitions.

TL;DR: A novel high-resolution multi-scale encoder–decoder network (HMEDN), in whichMulti-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information to accurately locate indistinct boundaries.