统计学双周学术论坛（第一期）
发布时间：2013-09-18
主题：Automated learning of factor analysis with complete and incomplete data
主讲人：赵建华 博士
时间：2013年9月18日（周三）下午15:00
地点：北院卓远楼305
主办单位：统计与数学学院
摘要：In the application of the popular maximum likelihood method to factor analysis, the number of factors is commonly determined through a two-stage procedure, in which stage 1 performs parameter estimation for a set of candidate models and then stage 2 chooses the best according to certain model selection criterion. Usually, to obtain satisfactory performance, a large set of candidates is used and this procedure suffers heavy computational burden. To overcome this problem, we propose in this paper a novel one-stage algorithm in which we integrate parameter estimation and model selection in a single algorithm. This is obtained by maximizing the criterion with respect to model parameters and the number of factors jointly, rather than separately. The proposed algorithm is then extended to accommodate incomplete data. Experiments on a number of complete/incomplete synthetic and real data reveal that the proposed algorithm is as effective as the existing two-stage procedure while being much more computationally efficient, particularly for incomplete data.
赵建华博士简介：
香港大学统计学博士，副教授。研究领域为统计机器学习与数据挖掘，在国际著名学术期刊发表论文多篇，包括《IEEE Transactions on Neural Networks and Learning Systems》等国际权威性刊物。
主讲人：赵建华 博士
时间：2013年9月18日（周三）下午15:00
地点：北院卓远楼305
主办单位：统计与数学学院
摘要：In the application of the popular maximum likelihood method to factor analysis, the number of factors is commonly determined through a two-stage procedure, in which stage 1 performs parameter estimation for a set of candidate models and then stage 2 chooses the best according to certain model selection criterion. Usually, to obtain satisfactory performance, a large set of candidates is used and this procedure suffers heavy computational burden. To overcome this problem, we propose in this paper a novel one-stage algorithm in which we integrate parameter estimation and model selection in a single algorithm. This is obtained by maximizing the criterion with respect to model parameters and the number of factors jointly, rather than separately. The proposed algorithm is then extended to accommodate incomplete data. Experiments on a number of complete/incomplete synthetic and real data reveal that the proposed algorithm is as effective as the existing two-stage procedure while being much more computationally efficient, particularly for incomplete data.
赵建华博士简介：
香港大学统计学博士，副教授。研究领域为统计机器学习与数据挖掘，在国际著名学术期刊发表论文多篇，包括《IEEE Transactions on Neural Networks and Learning Systems》等国际权威性刊物。
