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-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, for non-negative data (e.g., documents), we int...

in recent years, collaborative filtering (cf) methods are important and widely accepted techniques are available for recommender systems. one of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. however, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. with the dev...