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.

数字数据正在以无与伦比的速度生成大量的数字数据，并且数量过多。机器学习和数据挖掘是可以帮助我们跟上快速前进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监督学习，该学习使用未标记数据中的其他信息，也可以用无监督的学习为指导的，这些学习是由标记数据形成的约束所指导的。这项研究正在解决有关大量数据的两个关键紧迫问题：高维度和标记数据的短缺。特别是，该项目是：研究半监督特征选择以删除无关紧要的功能；研究特征提取和模型选择的组合，以进一步降低维度；并开发一个新颖的框架来集成基于稀疏学习的特征选择和特征提取。这项研究是一项明确的尝试，以连接和统一特征选择和提取假设空间的选择。该项目直接促进基本的机器学习研究和实践数据挖掘，并进步了创新的研究，超出了功能选择和提取。这项工作正在吸引学生参与教学和研究，算法，工具和数据库将被公开用于研究目的并用作教学资源。