Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Multi-view learning is an emerging direction in machine learning which considers learning
with multiple views to improve the generalization performance. Multi-view learning is also …

In this paper, we propose a new unsupervised domain adaptation approach called
Collaborative and Adversarial Network (CAN) through domain-collaborative and domain …

Traditional data mining usually deals with data from a single domain. In the big data era, we
face a diversity of datasets from different sources in different domains. These datasets …

In image analysis, the images are often represented by multiple visual features (also known
as multiview features), that aim to better interpret them for achieving remarkable …

A central theme in learning from image data is to develop appropriate image representations
for the specific task at hand. Traditional methods used handcrafted local features combined …

Cross-modal retrieval (CMR) enables flexible retrieval experience across different
modalities (eg, texts versus images), which maximally benefits us from the abundance of …

The rapid urbanization has motivated extensive research on urban computing. It is critical for
urban computing tasks to unlock the power of the diversity of data modalities generated by …

Multi-view unsupervised feature selection has been proven to be an effective approach to
reduce the dimensionality of multi-view data. One of its key issues is how to exploit the …

During the past two decades, multiview learning as an emerging direction in machine
learning became a prevailing research topic in artificial intelligence (AI). Its success and …