Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-
seq) data are ubiquitous in bioinformatics, but using single clustering or classification …

Optimization on Riemannian manifolds-the result of smooth geometry and optimization
merging into one elegant modern framework-spans many areas of science and engineering …

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …

Predicting patients' risk of developing certain diseases is an important research topic in
healthcare. Accurately identifying and ranking the similarity among patients based on their …

Drug recommendation is an important task of AI for healthcare. To recommend proper drugs,
existing methods rely on various clinical records (eg, diagnosis and procedures), which are …

Metric learning can be very useful to improve the performance of a distance-dependent
classifier. However, separating metric learning from the classifier learning possibly …

We propose a novel discriminative feature learning method via Max-Min Ratio Analysis
(MMRA) for exclusively dealing with the long-standing “worst-case class separation” …

We propose computationally tractable accelerated first-order methods for Riemannian
optimization, extending the Nesterov accelerated gradient (NAG) method. For both …

Metric learning based methods have attracted extensive interests in recommender systems.
Current methods take the user-centric way in metric space to ensure the distance between …

Given a group photograph, it is interesting and useful to judge whether the characters in it
share specific kinship relation, such as father-daughter, father-son, mother-daughter, or …