In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views fuse them by considering each view’s dynamic contribution obtained attention mechanism. Unlike most multi-view subspace learning methods that they directly reconstruct data points on raw or only consider consistency com...

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing methods operate raw feature space and heavily depend on quality original representation. Moreover, they are often designed for data ignore rich topology structure information. Accordingly, this paper, we propose a generic framework to cluster both attribute graph wi...

Multi-view clustering is an important approach for analyzing multi-view data in unsupervised way. Among various methods, the subspace has gained increasing attention due to its encouraging performance. Essentially, it integrates information into graphs, which are then fed spectral algorithm final results. However, performance may degrade noises existing each individual view or inconsistencies b...

Since advanced technologies via social media, internet, virtual communities and networks internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as natural design for data. K-means (KM) (single-view) had been extended handling data, called KM (MV-KM). In the literature, most MV-KM algorithms reported influenced by initializations als...

Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we pro...

Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multiview data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data ...

Abstract Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information different views, has brought along a growth attention. algorithms based on theories have been proposed and extended in various applications. However, most existing MVC are shallow models, learn multi-view mapping low-dimensional representation space directly, igno...