NEURAL NETWORK-ENHANCED STEREOSCOPIC VISUALIZATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 大量的高维数据不仅需要分析数据和解释结果，而且需要开发可以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的应用增强了知识提取，并针对复杂的高维数据，并提供了非常小的信息损失。作为该项目的一部分，我们研究的方法基于自组织映射算法，并使用用于高性能计算的OpenMP（用于共享内存）和/或MPI 2.0（用于分布式内存）库在C/C++编程语言中实现。 在神经网络增强的经典可视化中，记录的放置是由所有（或者，如果需要的话，一个子集）维值指导的。这意味着所有维度都对记录的整体布局有贡献，而不仅仅是像散点图、radviz、平行坐标、星星图等经典可视化中那样的一对维度。 虽然我们的工作重点是生物医学数据集，我们的算法已成功地应用于广泛的数据集，从微阵列到人口普查数据。

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.