Deep metric learning (DML) has been designed to maximize the inter-class variance that is
the distance between embedding features belonging to different classes. Since conventional …

For one given scene, multimodal data are acquired from multiple sensors. They share some
similarities across the sensor types (redundant part of the information, also called coupling …

Multimodal learning, also known as multi-view learning, data integration, or data fusion, is
an emerging field in signal processing, machine learning, and pattern recognition domains …

Neighborhood graph based nonlinear dimensionality reduction algorithms, such as Isomap
and LLE, perform well under an assumption that the neighborhood graph is connected …

In deep metric learning (DML) techniques, understanding both the local and global
characteristics of embedding space is essential. However, conventional DML techniques …

Dimension reduction is very important for pattern recognition. Preserving the manifold is a
popular way to enhance the effect of the dimension reduction method. However, most of the …

Exploring gene networks is crucial for identifying significant biological interactions occurring
in a disease condition. These interactions can be acknowledged by modeling the tie …