Multi-Source Visual Analytics

多源可视化分析

• 批准号: 1025177
• 负责人: Jieping Ye
• 金额: $ 49.85万
• 依托单位: Arizona State University
• 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: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025177&HistoricalAwards=false
• 关键词: Multi; Source; Visual; Analytics

AbstractData visualization forms an important aspect of analysis in the field of visual analytics. Analysts rely on visual tools to process massive data sets and discover meaningful patterns in the data. A common strategy for many visualization tools is to transform high-dimensional data to an intermediate lower-dimensional space and then project to screen space using a visualization transformation. For example, a data set with 200 dimensions can be transformed to an intermediate 4D representation and then mapped to screen space by using two-dimensionsfor the location and two dimensions to determine shape and color. Therefore, the mathematical foundations of visualization are closely related to the problem of dimensionality reduction.While dimensionality reduction is a necessary step to visualize the data, the final goal of visual analytics is data analysis, such as searching, clustering, and the detection of outliers. Therefore, there is an urgent need to study dimensionality reduction techniques that are especially useful for data analysis. This research involves the development and implementation of linear and nonlinear dimensionality reduction algorithms for the transformation and visualization of high-dimensional data. The novel aspect of the transformation is that dimensionality reduction and clustering are performed simultaneously in a joint framework. In addition, this research involves the development and implementation of novel algorithms for multi-source data transformations based on multiple kernel learning (MKL). This addresses the question of fusing a multitude of heterogeneous independently collected data. In the past, most research on MKL has focused on supervised learning. One major contribution of this research is to extend MKL to the unsupervised case. This research presents visual analytics as a bridge between theoretical foundations in machine learning and real-world applications. This research is utilizing two testbed data bases, one consisting of printed documents as might be used by the intelligence community and one based on public health information.

摘要数据可视化是可视化分析领域的一个重要方面。分析师依靠可视化工具来处理大量数据集并发现数据中有意义的模式。许多可视化工具的常见策略是将高维数据转换为中间的低维空间，然后使用可视化转换投影到屏幕空间。例如，具有200个维度的数据集可以被转换为中间4D表示，然后通过使用用于位置的二维和用于确定形状和颜色的二维来映射到屏幕空间。因此，可视化的数学基础与降维问题密切相关，降维是数据可视化的必要步骤，而可视化分析的最终目标是数据分析，如搜索，聚类和异常值检测。因此，迫切需要研究对数据分析特别有用的降维技术。这项研究涉及的线性和非线性降维算法的发展和实施的高维数据的转换和可视化。该变换的新颖之处在于，在联合框架中同时进行降维和聚类。此外，本研究还涉及基于多核学习（MKL）的多源数据转换新算法的开发和实现。这解决了融合大量独立收集的异构数据的问题。在过去，大多数关于MKL的研究都集中在监督学习上。这项研究的一个主要贡献是将MKL扩展到无监督的情况。这项研究将可视化分析作为机器学习理论基础和现实世界应用之间的桥梁。这项研究利用了两个试验数据库，一个由可能被情报界使用的印刷文件组成，另一个基于公共卫生信息。