Recently, collaborative representation classification (CRC) has attracted much attention for
hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved …

Multiview representation learning (MVRL) leverages information from multiple views to
obtain a common representation summarizing the consistency and complementarity in …

Multi-view visual classification methods have been widely applied to use discriminative
information of different views. This strategy has been proven very effective by many …

Multi-view learning concentrates on leveraging the consensus and complementarity
information among multiple distinct feature representations to improve the performance …

Background Large-scale collaborative precision medicine initiatives (eg, The Cancer
Genome Atlas (TCGA)) are yielding rich multi-omics data. Integrative analyses of the …

Multi-view learning concentrates on fully using the data collected from diverse domains or
obtained from various feature extractors to learn effectively. The consensus and …

Multi-view learning (MVL) concentrates on the problem of learning from the data
represented by multiple distinct feature sets. The consensus and complementarity principles …

Fine-grained image classification attempts to accurately classify images that are similar to
each other. Multiview information is often used to improve the classification accuracy …

We introduce a novel boosting algorithm called 'KTBoost'which combines k ernel boosting
and t ree boosting. In each boosting iteration, the algorithm adds either a regression tree or …

At present, the amount of network equipment, servers, and network traffic is increasing
exponentially, and the way in which operators allocate and efficiently utilize network …