Multi-view subspace clustering aims to discover the hidden structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of previous multi-view algorithms are still faced with two limitations. First, they usually focus on consistency (or commonness) views, yet often lack ability capture cross-view inconsi...

Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In ...

Clinical document contains vital information like symptom names, medication names, age, gender and some demographical information. These information can be used for giving quick relief from a disease. In existing system, they had built a system for clustering symptom names and medication names using Multi-View Non-Negative Matrix Factorization. While considering the clinical documents the facto...

We consider the problem of finding communities in large linked networks such as web structures or citation networks. We review similarity measures for linked objects and discuss the k-Means and EM algorithms, based on text similarity, bibliographic coupling, and co-citation strength. We study the utilization of the principle of multi-view learning to combine these similarity measures. We explor...

In this paper, we address the multi-view subspace clustering problem. Our method utilize the circulant algebra for tensor, which is constructed by stacking the subspace representation matrices of different views and then shifting, to explore the high order correlations underlying multi-view data. By introducing a recently proposed tensor factorization, namely tensor-Singular Value Decomposition...

In this paper we develop an algorithm for spectral clustering in the multi-view setting where there are two independent subsets of dimensions, each of which could be used for clustering (or classification). The canonical examples of this are simultaneous input from two sensory modalitites, where input from each sensory modality is considered a view, as well as web pages where the text on the pa...

Multi-view learning aims to improve classification performance by leveraging the consistency among different views of data. The incorporation of multiple views was paid little attention in the studies of domain adaptation, where the view consistency based on source data is largely violated in the target domain due to the distribution gap between different domain data. In this paper, we leverage...

Multi-view subspace clustering (MVSC) can effectively group multi-view data distributed around several low-dimensional subspaces. Although encouraging results, most existing methods suffer from two typical limitations, resulting in performance degradation. They ignore high-order correlations underlying the data, leading to degeneration of complementary power; addition, they rely on much prior k...

Abstract Graph learning is being increasingly applied to image clustering reveal intra-class and inter-class relationships in data. However, existing graph learning-based focuses on grouping images under a single view, which under-utilises the information provided by To address that, we propose self-supervised multi-view technique contrastive heterogeneous learning. Our method computes affinity...