Manifold Alignment of High-Dimensional Data Sets

高维数据集的流形对齐

• 批准号: 1025120
• 负责人: Sridhar Mahadevan
• 金额: $ 49.99万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025120&HistoricalAwards=false
• 关键词: Manifold; Alignment; Dimensional; Data; Sets

As the availability and size of digital information repositories continues to burgeon, the problem of extracting deep semantic structure from high-dimensional data becomes more critical. This project addresses the fundamental problem of transfer learning, in particular it investigates methods for aligning multiple heterogeneous data sets to find correspondences and extract shared latent semantic structure. Domains of applicability include automatic machine translation, bioinformatics, cross-lingual information retrieval, perceptual learning, robotic control, and sensor-based activity modeling. The proposed research will investigate a geometric framework for transfer learning based on finding correspondences between data by aligning their projections onto lower dimensional manifolds. The proposed research will investigate a broad spectrum of approaches to manifold alignment, including one-step vs. two-step alignment, instance-based vs. feature-based alignment, semi-supervised vs. unsupervised alignment, and finally one-level vs. multi-scale alignment. Visualization tools that use alignment information will be developed to facilitate interactive learning from data analysis. To aid the processing of large data sets, the parallel computational power of modern graphics processing units (GPUs) will be exploited.Given the rapidly increasing availability of digital data sets from a diverse variety of domains, the scientific question of extracting knowledge from massive unstructured information repositories is becoming ever more critical. The proposed research combines the study of machine learning algorithms for discovering latent correspondences between seemingly disparate data sets, and the development of visualization tools to aid human interpretation of high-dimensional data. Empirical studies on a variety of real-world applications will be carried out, ranging from bioinformatics, Internet web archives, multilingual text, and sequential time-series data sets. The broader impacts of the proposed research include algorithmic advances in the analysis and visualization of high-dimensional data, and empirical studies on a variety of real-world applications. The data sets and software developed in this research will be disseminated through the web. The research will be communicated through a variety of conferences, workshops and seminars in several disciplines ranging from computer science, engineering, mathematics, and statistics. The PIs will make significant efforts to recruit underrepresented groups, including women and other minorities, in this research. New course material on advanced data analysis and visualization will be developed based on the proposed research.

随着数字信息仓库的可用性和规模不断扩大，从高维数据中提取深层语义结构的问题变得更加关键。该项目解决了迁移学习的基本问题，特别是它研究了对齐多个异构数据集以找到对应关系并提取共享潜在语义结构的方法。适用领域包括自动机器翻译、生物信息学、跨语言信息检索、感知学习、机器人控制和基于传感器的活动建模。 拟议的研究将研究迁移学习的几何框架，该框架基于通过将数据投影对齐到低维流形上来寻找数据之间的对应关系。拟议的研究将调查各种各样的方法来流形对齐，包括一步与两步对齐，基于实例与基于特征的对齐，半监督与无监督对齐，最后是一级与多尺度对齐。将开发使用对齐信息的可视化工具，以促进数据分析的交互式学习。为了帮助处理大型数据集，现代图形处理单元（GPU）的并行计算能力将被利用。鉴于来自各种领域的数字数据集的可用性迅速增加，从大量非结构化信息库中提取知识的科学问题变得越来越重要。拟议的研究结合了机器学习算法的研究，用于发现看似不同的数据集之间的潜在对应关系，以及可视化工具的开发，以帮助人类解释高维数据。 将对各种实际应用进行实证研究，包括生物信息学、互联网网络档案、多语种文本和连续时间序列数据集。拟议研究的更广泛影响包括高维数据分析和可视化的算法进步，以及对各种现实应用的实证研究。在这项研究中开发的数据集和软件将通过网络传播。该研究将通过各种会议，研讨会和研讨会在几个学科，从计算机科学，工程，数学和统计沟通。研究所将作出重大努力，在这项研究中招募代表性不足的群体，包括妇女和其他少数民族。 新的课程材料先进的数据分析和可视化将开发基于拟议的研究。