Objective Discovering subphenotypes of complex diseases can help characterize disease
cohorts for investigative studies aimed at develo** better diagnoses and treatments …

Social media platforms like Twitter have become an easy portal for billions of people to
connect and exchange their thoughts. Unfortunately, people commonly use these platforms …

With the help of auxiliary data, tensor completion may better recover a low rank
multidimensional array from limited observation entries. Most existing methods, including …

Modeling the behaviors of drug-target-disease interactions is crucial in the early stage of
drug discovery and holds great promise for precision medicine and personalized treatments …

Coupled matrix and tensor factorizations (CMTF) are frequently used to jointly analyze data
from multiple sources, a task also called data fusion. However, different characteristics of …

Existing tensor completion formulation mostly relies on partial observations from a single
tensor. However, tensors extracted from real-world data often are more complex due to:(i) …

Missing data is an inevitable and ubiquitous problem in the data-driven Intelligent
Transportation System (ITS), which seriously affects the accuracy of urban traffic planning …

The relational model is a ubiquitous representation of big-data, in part due to its extensive
use in databases. In this paper, we propose the Equivariant Entity-Relationship Network …

Topic discovery has witnessed a significant growth as a field of data mining at large. In
particular, time-evolving topic discovery, where the evolution of a topic is taken into account …

Missing data imputation is crucial in finance to ensure accurate financial analysis, risk
management, investment strategies, and other financial applications. Recently, tensor …