Li Shang

ERMMA: Expected Risk Minimization for Matrix Approximation-based Recommender Systems

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

Matrix approximation (MA) is one of the most popular techniques in today's recommender systems. In most MA-based recommender systems, the problem of risk minimization should be defined, and how to achieve minimum expected risk in …

Data quality screening for high-resolution satellite imagery via spectral clustering

2017

High-resolution satellite imagery data have been widely used in geoscience and remote sensing research. Dealing with data quality issue is the first and most important step before truly making use of these high-resolution images. Scientific …