含信息保护的跨介质多域保似学习方法及其微生物数据之知识迁移利用研究

项目名称： 含信息保护的跨介质多域保似学习方法及其微生物数据之知识迁移利用研究

项目编号： No.61272210

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 王士同

作者单位： 江南大学

项目金额： 81万元

中文摘要： 本课题拟针对跨介质多域数据的知识迁移利用研究中所涉及的共性挑战问题，探究相应的新的学习理论和方法，并应用于微生物数据资源的知识挖掘与利用研究。归纳起来，本课题紧密结合智能识别，知识挖掘与微生物信息技术，拟探讨如下的几个关键问题：1)跨介质多域学习的问题定义及其表述的规范化；2)跨介质多域保似学习基础算法即对数势保似学习机等及其理论分析与性能评估；3)基于多域保似同态对偶变换机制和对数势保似学习机的大规模数据环境下跨介质多域可扩展快速学习算法及其理论分析与性能评估；4)分布式环境下的跨介质多域学习算法及其理论分析与性能评估；5)跨介质多域学习的信息保护机制研究；6)基于跨介质多域学习的大规模微生物发酵数据资源的知识迁移利用。课题组的前期工作为本课题的可行性提供了充分的准备。

中文关键词： 保似学习；隐私保护；跨介质数据；微生物数据；多域学习

英文摘要： In order to overcome the common challenges existing in the field of knowledge mining and leveraging for cross-media and multi-domain datasets (for example, microbiological datasets which include text data and/or image data), novel learning theory and methodology will be investigated in this project. In this project, the following key issues will be explored: 1) the formal definition of the concept of cross-media and multi-domain learning problems; 2) The basic algorithms （i.e., the novel logarithmic-potential similarity-preserving learning machines）and their theoretical analysis and performance evaluation for the cross-media and multi-domain learning; 3) The fast learning algorithms based on the cross-media and multi-domain similarity-preserving homomorphic dual transform and logarithmic-potential similarity-preserving learning machines for the large-scale cross-media and multi-domain learning problems and their theoretical analysis and performance evaluation; 4) The distributed learning algorithms for the cross-media and multi-domain learning under the multi-soure or distributed environment and their theoretical analysis and performance evaluation; 5) The information protection mechanisms for the cross-media and multi-domain learning; 6) The knowledge leveraging of large-scale microbial data resources by using

英文关键词： Similarity-preserving learning；Privacy protection；Cross-media data；Micro-biological data；Multi-domain learning