Network embedding has been proven to be helpful for many real-world problems. In this
paper, we present a scalable multiplex network embedding model to represent information …

Feature extraction for activity recognition in context-aware ubiquitous computing
applications is usually a heuristic process, informed by underlying domain knowledge …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

Statistical analysis and machine learning can help us understand and predict the collective
properties and performance of ensembles of molecules and nanostructures, while …

We present a signal representation framework called the sparse manifold transform that
combines key ideas from sparse coding, manifold learning, and slow feature analysis. It …

Classification domains such as those in medicine, national security and the environment
regularly suffer from a lack of training instances for the class of interest. In many cases …

Linear discriminant analysis (LDA) uses labeled samples for acquiring a discriminant
projection direction, by which data of different categories are separated into distinct groups …

The advent of large-scale pretrained language models (PLMs) has contributed greatly to the
progress in natural language processing (NLP). Despite its recent success and wide …

In this study, we develop a method for multi-task manifold learning. The method aims to
improve the performance of manifold learning for multiple tasks, particularly when each task …

Filamentary structures (one-dimensional manifolds) are ubiquitous in astronomical data
sets. Be it in particle simulations or observations, filaments are always tracers of a …