In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Hyperspectral image (HSI) consists of hundreds of continuous narrowbands with high
spectral correlation, which would lead to the so-called Hughes phenomenon and the high …

Learning from a limited number of labeled samples (pixels) remains a key challenge in the
hyperspectral image (HSI) classification. To address this issue, we propose a deep metric …

Introducing a handful of pairwise constraints into fuzzy clustering models to revise
memberships has been proven beneficial to boosting clustering performance. However …

Question answering over knowledge graph (KGQA) aims at answering natural language
questions posed over knowledge graphs (KGs). Moreover, multi-hop KGQA requires …

Hyperspectral image classification (HSIC) faces three major challenging issues, which are
generally overlooked. One is how to address the background (BKG) issue due to its …

Deep learning has shown superiority to extract more representative features from
multimedia data in recent years. Recently, the most typical graph convolutional networks …

Currently, the representation learning of a graph has been proved to be a significant
technique to extract graph structured data features. In recent years, many graph …

At present, nonparametric subspace classifiers, such as collaborative representation-based
classification (CRC) and sparse representation-based classification (SRC), are widely used …

Cross-modal remote sensing (RS) image retrieval aims to retrieve RS images using other
modalities (eg, text) and vice versa. The relationship between objects in the RS image is …