CRII: RI: Towards a Comprehensive Dynamic Subset Selection Framework

子集选择是从一个大的基础集合中找到一个信息项目的小子集的任务。序列数据，包括时间序列和有序数据，构成了现代数据集的重要组成部分，需要有效的子集选择技术。该项目开发了一个统一的序列子集选择框架，该框架包含动态模型和项目之间的关系，解决无监督和有监督问题，并处理多模态数据。该项目开发实用的视频和多媒体总结工具、有利于人在循环系统的人类信息选择模型，以及为学生提供跨学科研究培训和传播研究成果。该项目的研究成果可以影响几个领域，如机器学习、计算机视觉、信号处理、可视化和健康信息学。本研究通过解决三个重要问题，开发了一个综合的序列数据集子集选择框架：1)开发了新的序列子集选择算法，该算法结合了序列数据之间的动态模型和关系，并开发了有效的优化技术来解决问题；Ii)通过将度量学习推广到子集选择中，解决了无监督和有监督的序列子集选择问题，并解决了缺少正负项对的挑战；iii)提出了一种多模态序列子集选择框架，以有效地利用数据集中的所有模态。该项目汇集了来自稀疏和低秩恢复、凸优化、消息传递、度量学习和动态系统的工具来解决问题。本项目还设计了客观的评价方法来衡量方法的绩效。

Subset selection is the task of finding a small subset of informative items from a large ground set. Sequential data, including time-series and ordered data, form an important large part of modern datasets, requiring effective subset selection techniques. This project develops a unified framework for sequential subset selection that incorporates dynamic models and relationships among items, addresses both unsupervised and supervised problems and handles multi-modal data. The project develops practical tools for video and multi-media summarization, human information selection models that benefit human-in-the-loop systems, as well as providing interdisciplinary research training to students and disseminates research results. The research results from this project can impact several communities, such as machine learning, computer vision, signal processing, visualization, and health-informatics. This research develops a comprehensive framework for subset selection in sequential datasets by addressing three important problems: i)it develops novel algorithms for sequential subset selection that incorporate dynamic models and relationships among sequential data and develops efficient optimization techniques to solve the problem; ii) it addresses the problem of unsupervised and supervised sequential subset selection by generalizing metric learning for subset selection and by tackling the challenge of the lack of pairs of positive and negative items; and iii) it proposes a multi-modal sequential subset selection framework to effectively take advantage of all modalities in datasets. The project brings together tools from sparse and low-rank recovery, convex optimization, message passing, metric learning and dynamical systems to tackle the problem. The project also designs objective evaluation methods to measure the performance of the methods.