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...

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...

Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition. Generally, it makes use of the complementary information embedded in multiple views to improve clustering performance. Recent methods usually find a low-dimensional embedding of multi-view data, but often ignore s...

In this paper, we describe our experiments of search task for TRECVID 2007. This year we participated in the automatic video search subtask, and submitted six runs with different combination of approaches to NIST. Using the text only based search engine used in last year, the run F_A_1_JTU_FA_1_1 provides a baseline search result list. In order to bring up true relevant results, a multi-view ba...

The plenty information from multiple views data as well as the complementary information among different views are usually beneficial to various tasks, e.g., clustering, classification, de-noising. Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace. To recover the underlying subspace structure, the success of the sparse and/or low-r...

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, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise...

Most clustering algorithms produce a single clustering solution. This is inadequate for many data sets that are multi-faceted and can be grouped and interpreted in many different ways. Moreover, for high-dimensional data, different features may be relevant or irrelevant to each clustering solution, suggesting the need for feature selection in clustering. Features relevant to one clustering inte...