BIGDATA: Mid-Scale: DA: Distribution-based machine learning for high dimensional datasets

BIGDATA：中规模：DA：针对高维数据集的基于分布的机器学习

• 批准号: 1247658
• 负责人: Aarti Singh
• 金额: $ 100万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247658&HistoricalAwards=false
• 关键词: BIGDATA; Mid; Scale; DA; Distribution

Many applications call for representation and analysis of 'distributional' data sets where each data point is a collection of samples from a high dimensional distribution (as opposed to valuations of a typically vector valued random variable). In this setting, each data point can be modeled by a collection of distributions, one for each measured attribute. A concrete example of distributional data arises in the context of brain connectivity mapping. The human brain contains around a hundred billion neurons with several hundred trillion physical connections. Neuroimaging approaches, like Diffusion Spectrum Imaging attempt to visualize the underlying anatomical architecture of neural pathways by creating 3D probability distributions of water diffusion along nerve fiber bundles, called orientation distribution functions. The project aims to develop new statistical and algorithmic approaches to natural generalizations of a class of standard machine learning problems (where multi-dimensional vector valued data points are replaced by distributions), including techniques for measuring distances and inner products between distributional data points, estimating variants of entropy, mutual information, conditional mutual information, clustering distributional data, constructing low-dimensional embeddings of distributional data, and learning classifiers and function approximators from distributional data. The resulting methods will be evaluated on large diffusion scan imaging data sets (where the data point for each patient consists of 500,000 distributions). The novel machine learning approaches for descriptive and predictive modeling of distributional data resulting from this project are expected to benefit other scientific fields where data points can be naturally modeled by sets of distributions, which is a common situation in physics, psychology, economics, epidemiology, medicine, and social network-analysis. New distributional data set to be obtained at CMU to augment the data available from NTU are likely to allow other research groups to engage in research on big data analytics from distributional data. Release of open source software, video tutorials, research-training of graduate students contribute to the broader impacts of the project. Additional information about the project can be found at: http://www.autonlab.org/autonweb/20928.html.

许多应用程序都要求对“分布”数据集的表示和分析，其中每个数据点是来自高维分布的样本集合（与典型矢量有价值的随机变量的估值相反）。在这种情况下，每个数据点可以通过分布集合来建模，每个分布都适用于每个测量属性。分布数据的具体示例是在大脑连接映射的背景下出现的。 人脑包含大约一亿神经元，并具有数百万个身体连接。神经影像学方法，例如扩散光谱成像尝试通过创建沿神经纤维束的水扩散的3D概率分布（称为方向分布函数）来可视化神经途径的基本解剖结构。该项目旨在开发新的统计和算法方法来对一系列标准的机器学习问题进行自然概括（其中多维矢量值的数据点被分布替换），包括用于测量分布数据点之间距离和内部产品之间的技术，并估算了入学信息，估算互动的分布数据，构建分布数据，构建分布数据，构建数据，构建数据，构建数据，构建数据，构造数据，构建范围，构建数据，构建范围，构建数据，构建范围，构建范围，建立了分布数据，建立了分布数据，建立了分布数据，构建了分布数据。和分布数据的功能近似值。最终的方法将在大型扩散扫描成像数据集上进行评估（其中每个患者的数据点由500,000个分布组成）。预计由该项目产生的分布数据的描述性和预测性建模的新型机器学习方法有望使其他科学领域受益，在这些领域中，可以通过一组分布来自然建模数据点，这是物理学，心理学，经济学，流行病学，医学，医学和社会网络分析中的常见情况。 新的分销数据集将在CMU获得，以增强NTU可用的数据可能允许其他研究小组从分销数据中从事大数据分析的研究。发布开源软件，视频教程，研究生的研究培训为项目的更广泛影响做出了贡献。有关该项目的其他信息，请访问：http：//www.autonlab.org/autonweb/20928.html。