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.

许多应用要求表示和分析“分布”数据集，其中每个数据点是来自高维分布的样本的集合(与典型的向量值随机变量的估值相反)。在此设置中，每个数据点可以由分布集合建模，每个测量属性对应一个分布集合。分布数据的一个具体例子出现在大脑连接图的背景下。人类大脑包含大约1000亿个神经元，这些神经元具有数百万亿个物理连接。神经成像方法，如扩散光谱成像，试图通过创建沿神经纤维束的水扩散的3D概率分布，即方向分布函数，来可视化神经通路的潜在解剖结构。该项目旨在开发新的统计和算法方法来对一类标准机器学习问题(其中多维向量值数据点被分布取代)的自然概括，包括测量分布数据点之间的距离和内积，估计熵的变量，互信息，条件互信息，聚类分布数据，构造分布数据的低维嵌入，以及从分布数据学习分类器和函数逼近器。由此产生的方法将在大型扩散扫描成像数据集(其中每个患者的数据点由500,000个分布组成)上进行评估。由该项目产生的用于分布数据的描述性和预测性建模的新的机器学习方法有望惠及其他科学领域，在这些领域中，数据点可以通过分布集自然地建模，这在物理学、心理学、经济学、流行病学、医学和社会网络分析中是常见的情况。CMU将获得新的分布数据集，以补充NTU提供的数据，这可能会允许其他研究小组从事从分布数据进行大数据分析的研究。开放源码软件的发布、视频教程、研究生的研究培训都有助于该项目产生更广泛的影响。有关该项目的更多信息，请访问：http://www.autonlab.org/autonweb/20928.html.