题目： Large Scale Nonparametric Tensor Analysis
报告人： 徐增林 教授 电子科技大学
摘要： Tensor factorization is an important approach to multiway data analysis. Many popular tensor factorization approaches—such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)—amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g., missing data and binary data), and (iii) noisy observations and outliers. In this talk, I will introduce tensor-variate latent nonparametric Bayesian models for multiway data analysis models. We name these models InfTucker, which essentially conduct Tucker decomposition in an infinite feature space. To further make these models scalable to large data, we will also introduce various extensions, which take advantages of distributed computing techniques such as MapReduce (e.g., Hadoop and Spark), online learning, and data sampling. Finally, I will show some experimental results in real world applications, such as network modeling, access log analysis, and click through rate prediction.