Attributed network embedding aims to seek low-dimensional vector representations for
nodes in a network, such that original network topological structure and node attribute …

Industrial automation effectively reduces the human effort in various activities of the industry.
In many autonomous systems, object recognition plays a vital role. Thus, finding a solution …

Multi-target regression is a challenging task that consists of creating predictive models for
problems with multiple continuous target outputs. Despite the increasing attention on multi …

Multi-label feature selection is an efficient technique to alleviate the high dimensionality for
multi-label learning. Existing multi-label feature selection methods based on information …

In multi-label learning, different labels may have their own inherent characteristics for
distinguishing each other, in the meanwhile, exploiting the correlations among labels is …

Multi-label classification has been successfully applied to image annotation, information
retrieval, text categorization, etc. When the number of classes increases significantly, the …

This article develops a multi-view multi-label learning for incomplete data which are
ubiquitous with the usage of three kinds of representations including within-view …

A growing number of multi-view data arises naturally in many scenarios, including medical
diagnosis, webpage classification, and multimedia analysis. A challenge in learning from …

In order to process multi-view multi-label data sets, we propose global and local multi-view
multi-label learning (GLMVML). This method can exploit global and local label correlations …

Multi-view learning is an emerging field that aims to enhance learning performance by
leveraging multiple views or sources of data across various domains. By integrating …