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 …

Urban water quality is of great importance to our daily lives. Prediction of urban water quality
help control water pollution and protect human health. In this work, we forecast the water …

Learning from multi-view multi-label data has wide applications. Two main challenges
characterize this learning task: incomplete views and missing (weak) labels. The former …

Amidst advancements in feature extraction techniques, research on multi-view multi-label
classifications has attracted widespread interest in recent years. However, real-world …

In reality, learning from multi-view multi-label data inevitably confronts three challenges:
missing labels, incomplete views, and non-aligned views. Existing methods mainly concern …

Classifying human actions from varied views is challenging due to huge data variations in
different views. The key to this problem is to learn discriminative view-invariant features …

As a problem often encountered in real-world scenarios multi-view multi-label learning has
attracted considerable research attention. However due to oversights in data collection and …

In the field of multi-view multi-label learning, the challenges of incomplete views and missing
labels are prevalent due to the complexity of manual labeling and data acquisition errors …