NEURAL NETWORK-ENHANCED STEREOSCOPIC VISUALIZATION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 大量的高维数据不仅需要分析数据和解释结果，而且还需要开发能够处理这些数据的工具和方法。许多技术本质上是图形化的，能够一次表示少量变量。使用神经网络技术的信息可视化应用增强了知识提取，目标是复杂的高维数据，并提供非常小的信息损失。作为项目的一部分，我们研究的方法基于自组织映射算法，并使用C/C++编程语言实现，使用OpenMP(用于共享内存)和/或MPI 2.0(用于分布式内存)库来实现高性能计算。 在神经网络增强的经典可视化中，记录的放置由所有维值(或者，如果需要的话，可以是维值的子集)来指导。这意味着所有维度都有助于记录的整体布局，而不是像在散点图、RadViz、平行坐标、星图等经典可视化中那样只有几个维度。 虽然我们的工作重点是生物医学数据集，但我们的算法已成功应用于从微阵列到人口普查数据的广泛数据集。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Large amounts of high-dimensional data not only create the need for analysis of the data and interpretation of results, but also the need for development of tools and methods that can handle such data. Many techniques are graphical in nature with ability to represent a small number of variables at a time. Application of information visualization using neural network techniques enhances knowledge extraction and is targeted towards complex high-dimensional data and provides for a very small, if any, loss of information. The approaches we work on as part of the project are based on a self-organizing map algorithm and are implemented in C/C++ programming languages using OpenMP (for shared memory) and/or MPI 2.0 (for distributed memory) libraries for high-performance computing. Placement of records in neural-network augmented classic visualizations is directed by all (or, if so desired, a subset of) dimensional values. This means that all dimensions contribute to the overall layout of records, not just a couple as this is the case in classic visualizations such as scatter plot, RadViz, parallel coordinates, star plot, etc. While the focus of our efforts is on biomedical data sets, our algorithms have been successfully applied to a wide range of data sets from microarray to census data.