CAREER: Dimensionality Reduction for Multi-Label Classification

职业：多标签分类的降维

• 批准号: 0953662
• 负责人: Jieping Ye
• 金额: $ 40.15万
• 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: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953662&HistoricalAwards=false
• 关键词: CAREER; Dimensionality; Reduction; Multi; Label

Recent advances in high-throughput technologies have unleashed a torrent of data with a large number of dimensions. Examples include gene expression pattern images, microarray gene expression data, protein/gene sequences, and neuroimages. Dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information, is crucial for the analysis of these data. The goal of this project is to develop efficient and effective dimensionality reduction algorithms for multi-label classification. Multi-label dimensionality reduction poses a number of exciting research questions that will be studied in this project: How to fully exploit the class label correlation for effective dimensionality reduction? How to scale dimensionality reduction algorithms to large-scale multi-label problems? How to effectively combine dimensionality reduction with classification? How to derive sparse dimensionality reduction algorithms to enhance model interpretability? How to derive multi-label dimensionality reduction algorithms for multiple data sources? To address these questions, a hypergraph spectral learning formulation will be developed for multi-label dimensionality reduction, in which a hypergraph is used to capture the class label correlation. A joint learning formulation will be developed, in which dimensionality reduction and multi-label classification are performed simultaneously. In addition, a multi-source dimensionality reduction framework is developed for learning from multiple heterogeneous data sources. The success of this project will largely improve the state-of-the-art in dimensionality reduction for multi-label classification, and broaden this research area by opening up and addressing many new research themes. The algorithms and tools developed in this project will directly impact biological research, as they will be used to annotate FlyExpress images; FlyExpress is the only digital library of standardized fruit fly embryonic expression patterns. The educational component of this project includes developing a new curriculum that incorporates research into the classroom and provides students from under-represented groups with opportunities to participate research. Project results, including open source software and data sets will be disseminated via project Web site (http://www.public.asu.edu/~jye02/Project/CAREER).

高通量技术的最新进展释放了具有大量维度的数据洪流。例子包括基因表达模式图像、微阵列基因表达数据、蛋白质/基因序列和神经图像。降维是通过去除不相关、冗余和噪声信息来提取少量特征的方法，对这些数据的分析至关重要。本课题的目标是为多标签分类开发高效的降维算法。多标签降维提出了许多令人兴奋的研究问题，这些问题将在本项目中进行研究：如何充分利用类标签相关性进行有效降维？如何将降维算法扩展到大规模的多标签问题？如何有效地将降维与分类结合起来？如何推导稀疏降维算法来增强模型的可解释性？如何为多个数据源导出多标签降维算法？为了解决这些问题，将开发用于多标签降维的超图谱学习公式，其中使用超图来捕获类标签相关性。将开发一种联合学习公式，其中同时进行降维和多标签分类。此外，还开发了用于从多个异构数据源学习的多源降维框架。该项目的成功将极大地提高多标签分类降维技术的发展水平，并通过开辟和解决许多新的研究主题来拓宽这一研究领域。在这个项目中开发的算法和工具将直接影响生物学研究，因为它们将被用来注释FlyExpress的图像；FlyExpress是唯一一个标准化果蝇胚胎表达模式的数字图书馆。该项目的教育部分包括开发一种新的课程，将研究纳入课堂，并为来自代表性不足群体的学生提供参与研究的机会。项目结果，包括开放源码软件和数据集将通过项目网站（http://www.public.asu.edu/~jye02/Project/CAREER）传播。