CAREER: Dimensionality Reduction for Multi-Label Classification

职业：多标签分类的降维

• 批准号: 1538638
• 负责人: Jieping Ye
• 金额: $ 27.89万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538638&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? The project is expected to 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. In order to achieve the project's goals, 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. Project results, including open source software and data sets will be disseminated via project Web site (http://www.yelab.net/projects/career).

高通量技术的最新进展释放了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。通过去除不相关的、冗余的和噪声的信息来提取少量特征的模糊性约简对于这些数据的分析至关重要。该项目的目标是开发高效的多标签分类降维算法。多标签降维提出了一些令人兴奋的研究问题，将在这个项目中研究：如何充分利用类标签的相关性有效的降维？如何将降维算法扩展到大规模多标签问题？ 如何将联合收割机降维与分类有效结合？如何推导稀疏降维算法以增强模型的可解释性？ 如何推导出适用于多数据源的多标签降维算法？该项目预计将在很大程度上提高最先进的多标签分类降维，并通过开放和解决许多新的研究主题来拓宽这一研究领域。该项目开发的算法和工具将直接影响生物学研究，因为它们将用于注释FlyExpress图像; FlyExpress是唯一的标准化果蝇胚胎表达模式的数字库。该项目的教育部分包括制定一个新的课程，将研究纳入课堂，并为代表性不足群体的学生提供参与研究的机会。 为了实现该项目的目标，将开发一个超图谱学习公式用于多标签降维，其中超图用于捕获类别标签相关性。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，一个多源降维框架的开发，从多个异构数据源的学习。项目成果，包括开放源码软件和数据集，将通过项目网站（http：//www.yelab.net/projects/career）传播。