Graph-based multi-view clustering (G-MVC) constructs a graphical representation of each view and then fuses them to unified graph for clustering. Though demonstrating promising performance in various applications, we observe that their formulations are usually non-convex, leading local optimum. In this paper, propose novel MVC algorithm termed robust graph-based (RG-MVC) address issue. particul...

in recent years wireless sensor networks have attracted researchers' attention due to vast applications. one of the most prominent issues regarding these networks is coverage which is considered as a quality of service parameter. in this study, we try to address energy shortage in wsns through finding a set of optimal coverage nodes in a specific environment. this is performed utilizing effecti...

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...

Wide-area 3D data generation for complex urban environments often needs to leverage a mixed use of collected from both air and ground platforms, such as aerial surveys, satellite, mobile vehicles. On one hand, kind with information drastically different views (ca. 90° more) forming cross-view data, which due very limited overlapping region caused by the line sight sensors, is difficult be regis...

Graph-based methods have hitherto been used to pursue the coherent patterns of data due its ease implementation and efficiency. These increasingly applied in multi-view learning achieved promising performance various clustering tasks. However, despite their noticeable empirical success, existing graph-based may still suffer suboptimal solution considering that can be very complicated raw featur...

Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve recommendation. However, it remains addressed how fully incorporate the rich auxiliary/side information effectively deal with heterogeneity in it. In this paper, we propose a M ul...