Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

Multimodal fusion focuses on integrating information from multiple modalities with the goal of
more accurate prediction, which has achieved remarkable progress in a wide range of …

View missing and label missing are two challenging problems in the applications of multi-
view multi-label classification scenery. In the past years, many efforts have been made to …

In recent years, multi-view multi-label learning has aroused extensive research enthusiasm.
However, multi-view multi-label data in the real world is commonly incomplete due to the …

As we all know, multi-view data is more expressive than single-view data and multi-label
annotation enjoys richer supervision information than single-label, which makes multi-view …

Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …

Existing multi-label learning approaches mainly utilize an identical data representation
composed of all the features in the discrimination of all the labels, and assume that all the …

As a combination of emerging multi-view learning methods and traditional multi-label
classification tasks, multi-view multi-label classification has shown broad application …

Multi-view learning has become a popular research topic in recent years, but research on
the cross-application of classic multi-label classification and multi-view learning is still in its …

In the multi-view multi-label (MVML) classification problem, multiple views are
simultaneously associated with multiple semantic representations. Multi-view multi-label …