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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

基本信息

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

项目摘要

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和无数的维度产生。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掘金或模式。半监督学习可以解释为使用来自未标记数据的附加信息的监督学习，或者由标记数据形成的约束指导的无监督学习。本研究解决了海量数据的两个关键紧迫问题：高维度和缺乏标记数据。具体来说，这个项目是：研究半监督特征选择，去除不相关的特征；研究特征提取与模型选择相结合，进一步降维；并开发了一种基于稀疏学习的特征选择与特征提取相结合的框架。本研究是将假设空间约简的特征选择和提取联系起来并统一起来的明确尝试。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以外的创新研究。这项工作让学生参与教学和研究，算法、工具和数据库将公开，用于研究目的和作为教学资源使用。