Abstract Hashing techniques, also known as binary code learning, have recently attracted increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For tasks, hashing research mainly mapped the original into Hamming space while heavily ignori...

Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the data containing missing in some views. Previous IMVC methods suffer from following issues: (1) inaccurate imputation or padding for negatively affects performance, (2) quality of features after fusion might be interfered by low-quality views, especially imputed To avoid these issues, this work presents im...

Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the incremental approaches are used for single vi...

Multi-view clustering has gained importance in recent times due to the large-scale generation of data, often from multiple sources. refers a set objects which are expressed by features, known as views, such movies being list actors or textual summary its plot. Co-clustering, on other hand, simultaneous grouping data samples and features under assumption that exhibit pattern only subset features...

This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike existing methods that all adopt an off-the-shelf norm without considering special characteristics in MVSC, we design a novel structured tailored to MVSC. Specifically, explicitly impose symmetric constraint and sparse frontal horizontal slices characterize intra-view inter-view rel...

Multi-view data clustering attracts more attention than their single view counterparts due to the fact that leveraging multiple independent and complementary information from multi-view feature spaces outperforms the single one. Multi-view Spectral Clustering aims at yielding the data partition agreement over their local manifold structures by seeking eigenvalue-eigenvector decompositions. Amon...

Most recently, tensor-SVD is implemented on multi-view self-representation clustering and has achieved the promising results in many real-world applications such as face clustering, scene clustering and generic object clustering. However, tensor-SVD based multi-view self-representation clustering is proposed originally to solve the clustering problem in the multiple linear subspaces, leading to...

Efficiency and robustness are the important performance for the registration of multi-view point sets. To address these two issues, this paper casts the multi-view registration into a clustering problem, which can be solved by the extended K-means clustering algorithm. Before the clustering, all the centroids are uniformly sampled from the initially aligned point sets involved in the multi-view...