Collaborative Research: Collaborative Learning for Multimodal Data

协作研究：多模态数据的协作学习

• 批准号: 1712564
• 负责人: Xiaotong Shen
• 金额: $ 30万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712564&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Multimodal; Data

A multimodal paradigm has become increasingly important given today's explosive growth of information, which often arises from, for instance, automatic image categorization and personalized prediction. Multimodal data has a wide spectrum of applications in medical diagnostics, social networking, multimedia, information filtering, personalized advertising, consumers' recommendations, virtually in any electronic commerce and entertainment platform. This research aims to develop statistical theory, methods, and computational tools to integrate multimodal data for prediction and description. The development will lead to the higher accuracy of learning, which will ultimately enhance information storage, sorting and filtering. Moreover, the research project has an education component to train graduate students in emerging areas. The research products will be disseminated through publications and presentations.The proposed research aims to develop statistical techniques to utilize conditional dependence structures for integrating multimodal data. It will proceed in the areas of collaborative learning and personalized prediction. In each area, regression, classification, and ranking will be performed collaboratively based on pairwise conditional dependencies between the response components, modeled by a directed graph or an undirected graph. Special efforts will be devoted to the joint learning of data of multiple modalities and extraction of latent structures with an adjustment for covariates. Target applications include image categorization and recommender systems, where the proposed techniques will be applied to understand the content of an image and to predict personalized preference over a large number of items. Furthermore, The research will develop computational tools and design methods that have desirable statistical properties.

鉴于当今信息的爆炸式增长，多模式范式变得越来越重要，这些信息通常来自自动图像分类和个性化预测。多模态数据在医疗诊断、社交网络、多媒体、信息过滤、个性化广告、消费者推荐等方面具有广泛的应用，几乎可以在任何电子商务和娱乐平台中使用。本研究旨在发展统计理论、方法和计算工具，以整合多模态数据进行预测和描述。这一发展将导致学习的准确性更高，最终将增强信息的存储、排序和过滤。此外，该研究项目有一个教育组成部分，以培养新兴领域的研究生。研究成果将通过出版物和简报传播。本研究旨在发展统计技术，利用条件依赖结构来整合多模态数据。它将在协作学习和个性化预测领域继续发展。在每个领域，回归、分类和排序将基于响应组件之间的成对条件依赖关系协同执行，由有向图或无向图建模。特别的努力将致力于多模态数据的联合学习和潜在结构的提取与协变量的调整。目标应用包括图像分类和推荐系统，其中所提出的技术将用于理解图像的内容并预测大量项目的个性化偏好。此外，该研究将开发具有理想统计特性的计算工具和设计方法。