Short CV 

Dr Jianyong Sun is now a professor at School of Mathematics and Statistics, Xi’an Jiaotong University. He is one of the award winners of the 12th “1000 Young Talent” programme. Before this post, he was a senior lecturer in Faculty of Engineering, University of Greenwich. 

His research spans both theoretical and practical aspects of artificial intelligence, mainly on machine learning, statistical modelling, meta-heuristic, evolutionary optimisation, computational biology/bioinformatics, and image processing. His current research interests include, but not limited to, machine learning (algorithms and learning theories) on big data;and evolutionary optimization for large-scale (problem dimension ≥ 1000) optimization problems. 

He has published more than 40 journal and conference papers on prestigious international journals such as IEEE Trans on Evolutionary Computation, IEEE Trans on Cybernetics, IEEE Trans. On Neural Networks and Learning Systems, Proceedings of the National Academic Sciences (PNAS), IEEE/ACM Trans on Computational Biology and Bioinformatics, etc, and top-tier conferences such as International Conference on Machine Learning, and Congress on Evolutionary Computation, etc. He serves as PC members for more than 15 conferences, and regular reviewer/editor for many prestigious international journals. 

Report Title 

Simultaneous Bayesian Clustering and Feature Selection Through Student's t Mixtures Model 

Report Abstract 

In this paper, we proposed a generative model for feature selection under the unsupervised learning context. The model assumes that data are independently and identically sampled from a finite mixture of Student's t distributions, which can reduce the sensitiveness to outliers. Latent random variables that represent the features' salience are included in the model for the indication of the relevance of features. As a result, the model is expected to simultaneously realise clustering, feature selection and outlier detection. Inference is carried out by a tree-structured variational Bayes (VB) algorithm. Full Bayesian treatment is adopted in the model to realise automatic model selection. Controlled experimental studies showed that the developed model is capable of modelling the data set with outliers accurately. Further, experiment results showed that the developed algorithm compares favourably against existing unsupervised probability model-based Bayesian feature selection algorithms on artificial and real data sets. Moreover, the application of the developed algorithm on real leukaemia gene expression data indicated that it is able to identify the discriminating genes successfully. 

主持人：蒋勇 

时间：2016年9月9日 15:00-16:00 

地点： 尚贤楼108报告厅 

欢迎参加！ 

数学与统计学院 

2016年9月7日