The thesis Robust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers by Li Chunna, an associate professor of Hainan University (HNU), was published in a recent issue of IEEE Transactions on Neural Networks and Learning Systems.

IEEE Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal of the IEEE Computational Intelligence Society. It publishes high-level articles that deal with the theories, designs, and applications of neural networks and related learning systems.

Large data scale and low value density are the main features of big data. Li's thesis proposes a robust and sparse linear discriminant analysis via an alternating direction multiplier algorithm. The method can extract the feature of any number effectively, and avoid the problem of small samples.

Li is a newly introduced high-level talent for HNU's Optimization and Data Intelligence Laboratory. Over the past two years, the laboratory has published more than 20 papers in IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Neural Networks, Information Sciences, Neurocomputing, Knowledge-Based Systems and other high-level Scientific Citation Index (SCI) journals, as well as undertaken three National Natural Science Foundation projects.

Li Chunna's thesis Robust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers.