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 …

Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

Recently, multi-view representation learning has become a rapidly growing direction in
machine learning and data mining areas. This paper introduces two categories for multi …

The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …

The inconsistency between textual features and visual contents can cause poor image
search results. To solve this problem, click features, which are more reliable than textual …

Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …

Compared with single-label image classification, multi-label image classification is more
practical and challenging. Some recent studies attempted to leverage the semantic …

Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL
framework is composed of two components:(1) Pre-train. Employ the base data to generate …

In recent years, researchers pay growing attention to the few-shot learning (FSL) task to
address the data-scarce problem. A standard FSL framework is composed of two …

Recently, a multi-view dictionary learning (DL) technique has received much attention.
Although some multi-view DL methods have been presented, they suffer from the problem of …