III-COR-Small: Beyond Feature Selection and Extraction - An Integrated Framework for High-Dimensional Data of Small Labeled Samples

III-COR-Small：超越特征选择和提取 - 小标记样本高维数据的集成框架

• 批准号: 0812551
• 负责人: Huan Liu
• 金额: $ 43.06万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812551&HistoricalAwards=false
• 关键词: III; COR; Small; Beyond; Feature

A plethora of digital data is being generated at unparalleled speed with an inordinate number of dimensions. Machine learning and data mining are approaches that can assist us in keeping pace with the rapidly advancing data gathering and storage techniques and help us mine nuggets or patterns from high-dimensional data. Semi-supervised learning can be interpreted as supervised learning that uses additional information from unlabeled data, or as unsupervised learning guided by constraints formed from labeled data. This research is addressing two key pressing issues with massive data: high dimensionality and a shortage of labeled data. In particular, this project is: investigating semi-supervised feature selection to remove irrelevant features; studying the combination of feature extraction and model selection to further reduce dimensionality; and developing a novel framework to integrate feature selection and feature extraction based on sparse learning. This study is an explicit attempt to connect and unify feature selection and extraction for hypothesis space reduction. The project is directly facilitating basic machine learning research and practical data mining and advances innovative research beyond feature selection and extraction. The work is engaging students in both teaching and research, and the algorithms, tools and databases will be made publically available for research purposes and for use as teaching resources.

大量的数字数据正在以无与伦比的速度生成，其维度数量过多。机器学习和数据挖掘是可以帮助我们跟上快速发展的数据收集和存储技术的方法，并帮助我们从高维数据中挖掘出金块或模式。半监督学习可以被解释为使用来自未标记数据的附加信息的监督学习，或者被解释为由标记数据形成的约束引导的无监督学习。这项研究解决了海量数据的两个关键紧迫问题：高维和标记数据的短缺。具体而言，本项目是：研究半监督特征选择，以去除不相关的特征;研究特征提取和模型选择的结合，以进一步降低维数;并开发一种新的框架，集成特征选择和特征提取基于稀疏学习。这项研究是一个明确的尝试，连接和统一的特征选择和提取的假设空间减少。该项目直接促进了基础机器学习研究和实用数据挖掘，并推动了特征选择和提取之外的创新研究。这项工作正在让学生参与教学和研究，算法，工具和数据库将被用于研究目的并用作教学资源。