学术报告
同济大学计算机科学与技术系智信讲坛第(85)期
题目：Coherent Pattern Detection in Multidimensional Big Data
报告人：Hong Yan
时间： 2017年03月07日15点
地点：济人楼405会议室
组织单位：计算机科学与技术系
邀请人：赵兴明教授
报告人简介：Hong Yan received his PhD degree from Yale University. He was Professor of Imaging Science at the University of Sydney and is currently Chair Professor of Computer Engineering at City University of Hong Kong. His research interests include image processing, pattern recognition and bioinformatics. Professor Yan was elected an IAPR fellow for contributions to document image analysis and an IEEE Fellow for contributions to image recognition techniques and applications. He received the 2016 Norbert Wiener Award from IEEE SMC Society for contributions to image and biomolecular pattern recognition techniques.
内容提要：Multidimensional datasets can be very big in size, but they may contain much smaller meaningful patterns. In a large matrix, we can perform data classification in either feature or object direction based on traditional clustering algorithms. However, if a coherent pattern embedded in the data involves a subset of features and a subset of objects, then biclustering analysis is needed, which is often more complicated than clustering. The problem is even more challenging if the data dimensionality is large. For example, in gene expression data, we may be interested in extracting a subset of genes that co-express under a subset of conditions at a subset of time points. In consumer data analysis, we may want to find a subset of consumers who like a subset of products in a subset of locations. In these two cases, we need to analyze three dimensional data arrays, or perform triclustering. Recently, we have discovered that a class of coherent patterns in multidimensional data can be represented as hyperplanes in singular vector spaces. By decomposing a data array into singular vector matrices, we can then deal with pattern coherence in individual directions. We have applied our coherent pattern detection algorithms successfully to genomic data analysis, protein secondary structure identification, disease diagnosis, drug therapeutic effect assessment, and human facial expression analysis. Our method can also be useful for many other real world data mining and pattern recognition applications.
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