报告题目：变分贝叶斯Logistic张量回归在图像识别中的应用

主讲嘉宾：金运志 博士

时间：2026年4月9日下午16:55    

地点：格物楼3103室

摘要：In this talk, we develop a novel variational Bayesian method for image classification in a logistic tensor regression model with image tensor predictors by utilizing tensor decomposition to approximate tensor regression. To handle the sparsity of tensor coefficients, we introduce the multiway shrinkage priors for marginal factor vectors of tensor coefficients. In particular, we obtain a closed-form approximation to the variational posteriors for classification prediction based on the matricization of tensor decomposition. Simulation studies are conducted to investigate the performance of the proposed methodologies in terms of accuracy, precision and F1 score. Flower image data and chest X-ray image data are illustrated by the proposed methodologies.

嘉宾介绍：金运志，2017年博士毕业于云南大学，随后在云南大学统计学博士后流动站从事科研工作，主要研究方向为图像识别、机器学习、贝叶斯分析。

初审｜韩芳宇

复审｜鲁学伟

终审｜杨汉春