ATD: Next Generation Statistical Learning Theory and Methods for Multimodal Spatio-Temporal Data with Application to Computer Vision

ATD：下一代多模态时空数据统计学习理论和方法及其在计算机视觉中的应用

基本信息

批准号：
1924724
负责人：
Tapabrata Maiti
金额：
$ 49.95万
依托单位：
Michigan State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924724&HistoricalAwards=false
关键词：
ATD Next Generation Statistical Learning

项目摘要

The main purpose of this project is to develop next-generation statistical theory and methods of machine learning for new age spatio-temporal data that are arising in technology-based modern world contexts such as computer vision, self-driving cars, and imaging. The key focus of traditional statistical theory and modeling for spatial data lies mostly in interpolation and prediction within the study region. Further, the available techniques are restrictive because of many strong assumptions. Besides new ways of modeling spatio-temporal data, this project considers the classification and prediction of spatial objects. The rapid development of information technology is making it possible to collect massive amounts of data in multiple modalities, posing serious challenges to data scientists for multi-tasking the tremendous amount of data in real time. Conventional vector-based statistical modeling is computationally inefficient and inadequate for the classification of spatial objects in complex and high dimensional contexts. This project provides a broader and nonstandard framework for handling such massive spatio-temporal data. The proposed theory and methods are grounded with computer vision applications with low training sample. The project will provide training to undergraduate and graduate students. This project aims at providing two innovative ways of modeling spatio-temporal data, artificial neural networks, and tensor and classifying spatial objects. The techniques are well established in applied machine learning literature but distinguish themselves from the traditional spatio-temporal analysis in statistics. The proposed research builds upon capturing spatio-temporal dependence using machine learning techniques to avoid modeling large covariance matrix and capture complex spatio-temporal dependence. The techniques avoid specifying big covariance matrices to make the models computationally efficient and rely on less distributional assumptions. The proposed mathematical foundations for these methods not only develop new statistical theories, but also eliminate the value loss of these machine learning methods due to lack of adequate mathematical justifications. Another important feature of this project is that this considers computer vision applications which generally come with low training sample. The popular machine learning techniques such as deep net, neural net, or higher order tensors require large training sample for building effective systems. A major thrust of this project is to overcome this issue by adopting several dimension reduction techniques that can handle high-dimensional spatio-temporal data with small sample size without overfitting the model. The project will advance research in high dimensional machine learning theory and methods. The theory and methods developed in this project serve a general framework of dealing with large and complex spatio-temporal data and has broader impacts in multidisciplinary fields including statistics, computer science, neuroimaging, machine learning, and data science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的主要目的是为计算机视觉、自动驾驶汽车和成像等基于技术的现代世界环境中出现的新时代时空数据开发下一代统计理论和机器学习方法。传统空间数据统计理论和建模的重点主要在于研究区域内的插值和预测。此外，由于许多强有力的假设，可用的技术受到限制。除了时空数据建模的新方法之外，该项目还考虑了空间对象的分类和预测。信息技术的快速发展使得以多种方式收集大量数据成为可能，这对数据科学家实时处理大量数据提出了严峻的挑战。传统的基于向量的统计建模计算效率低下，并且不足以对复杂和高维环境中的空间对象进行分类。该项目提供了一个更广泛的非标准框架来处理如此海量的时空数据。所提出的理论和方法以低训练样本的计算机视觉应用为基础。该项目将为本科生和研究生提供培训。该项目旨在提供两种创新的时空数据建模方法、人工神经网络以及张量和空间对象分类。这些技术在应用机器学习文献中已经很成熟，但与统计学中传统的时空分析不同。所提出的研究建立在使用机器学习技术捕获时空依赖性的基础上，以避免建模大型协方差矩阵并捕获复杂的时空依赖性。这些技术避免指定大的协方差矩阵，以使模型计算高效并依赖较少的分布假设。这些方法所提出的数学基础不仅发展了新的统计理论，而且消除了这些机器学习方法由于缺乏足够的数学论证而造成的价值损失。该项目的另一个重要特征是，它考虑了通常训练样本较少的计算机视觉应用。流行的机器学习技术，如深度网络、神经网络或高阶张量，需要大量的训练样本来构建有效的系统。该项目的主要目标是通过采用多种降维技术来克服这个问题，这些技术可以处理小样本量的高维时空数据，而不会过度拟合模型。该项目将推进高维机器学习理论和方法的研究。该项目开发的理论和方法服务于处理大型复杂时空数据的总体框架，在统计学、计算机科学、神经影像、机器学习和数据科学等多学科领域具有更广泛的影响。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。