Multi-view subspace clustering under a tensor framework remains challenging problem, which can be potentially applied to image classification, impainting, denoising, etc. There are some existing tensor-based multi-view models mainly making use of the consistency in different views through nuclear norm (TNN). The diversity means intrinsic difference individual view is always ignored. In this pap...

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore important information hidden in missing and latent intrinsic structures each view. To tackle these issues, this paper, unified novel framework, named tensorized with graph completion (TIMVC_IGC) is proposed. Firstly, owing to effectiveness low-rank r...

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...

The ways of placing decision making units (DMUs) in certain clusters are found as a subject in statistics, these ways usually are heuristic. The proposed clustering approach in this article considers preferences of DMUs. This study applies Data Envelopment Analysis (DEA) DMUs are clustered by solving multi-objective linear problem (MOLP) and by considering preferences of each DMU at production ...

Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views. Relyi...

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...