项目名称: 基于概率图的判别式关系隐层空间模型研究
项目编号: No.61305066
项目类型: 青年科学基金项目
立项/批准年度: 2014
项目学科: 自动化技术、计算机技术
项目作者: 陈宁
作者单位: 清华大学
项目金额: 26万元
中文摘要: 隐层空间模型可以有效发掘复杂数据的隐含特征表示,已广泛用于关系网络数据分析。面向互联网环境下的海量复杂关系网络数据,本项目拟系统解决基于概率图的关系隐层空间模型中存在的模型表示、学习与推理、以及模型复杂度等若干基础性关键问题。更具体地说,本项目将:(1)提出基于后验正则化的广义关系隐层空间模型,提高传统关系隐层空间模型在描述对称和非对称关系网络以及包含实体对象属性的关系网络数据的能力和灵活性,克服关系网络中广泛存在的数据不均衡问题;(2)提出关系隐层空间模型的判别式最大间隔学习方法以及基于数据增广统计思想的"精确"推理算法,提高关系隐层空间模型学习判别性的特征表示的能力及其在链接预测、网络推荐、文本检索等任务中的预测性能;(3)提出非参数化的广义关系隐层空间模型,自动确定隐含特征的数目(即模型复杂度),克服参数化隐层空间模型需要时间代价很高的模型选择的缺陷。
中文关键词: 隐层空间模型;最大间隔学习;关系学习;非参数化贝叶斯推理;
英文摘要: Latent subspace models could effectively discover latent feature representations of complex data, and they have been widely used in analyzing relational network data. In order to handle the large-scale complex relational network data widely available in the Internet, this project aims to address some key basic issues of probabilistic relational latent subspace models in the aspects of model representation, learning and inference, model complexity control, and etc. More specifically, this project proposes to do the following work to improve existing relational latent subspace models. First, to improve the expressiveness and flexibility on modeling both symmetric and asymmetric networks, as well as the networks with entity attributes, and to overcome the common issue of data imbalance, this project proposes to learn a generalized relational latent subspace model, by exploring the ideas of posterior regularization and introducing a regularization parameter to well balance the data. Second, to improve the ability in learning predictive latent feature representations and the prediction performance in various tasks (such as link prediction, recommendation, document retrieval, etc.), this project proposes to do discriminative learning for the generalized relational latent subspace model, and also to develop an "accurat
英文关键词: Latent Subspace Model;Maximum Margin Learning;Relational Learning;Bayesian Nonparametric Methods;