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新时代时空数据开发下一代统计理论和机器学习方法，这些数据在基于技术的现代世界环境（例如计算机视觉，自动驾驶汽车和成像）中引起。传统统计理论和空间数据建模的重点主要在于研究区域内的插值和预测。此外，由于许多有力的假设，可用的技术是限制性的。除了建模时空数据的新方法外，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有可能以多种方式收集大量数据，从而对数据科学家提出了严重的挑战，以实时多任务数据。传统的基于向量的统计建模在计算上效率低下，并且不适用于在复杂和高维环境中的空间对象分类。该项目提供了一个更广泛的，非标准的框架，用于处理如此大规模的时空数据。所提出的理论和方法与训练样本低的计算机视觉应用结合在一起。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方法来建模时空数据，人工神经网络以及张量和分类空间对象。这些技术在应用机器学习文献中得到了很好的确定，但与统计中传统的时空分析区分开来。提出的研究基于使用机器学习技术捕获时空依赖性，以避免对大型协方差矩阵进行建模并捕获复杂的时空依赖性。这些技术避免指定大协方差矩阵，以使模型在计算上有效，并依赖于较少的分布假设。这些方法提出的数学基础不仅发展了新的统计理论，而且由于缺乏足够的数学理由而消除了这些机器学习方法的价值损失。该项目的另一个重要特征是，这考虑了通常带有低训练样本的计算机视觉应用程序。流行的机器学习技术，例如深网，神经网或高阶张量，需要大量的培训样本来构建有效系统。该项目的一个主要目的是通过采用几种可以处理高维时空数据而不适合模型的高维时空数据来克服此问题。该项目将推进高维机器学习理论和方法的研究。该项目中开发的理论和方法是处理大型和复杂时空数据的一般框架，并在多学科领域对包括统计，计算机科学，神经影像学，机器学习和数据科学等多学科领域产生了更大的影响。该奖项反映了NSF的法定任务，并通过使用基金会的智力效果和广阔的范围来评估支持NSF的法定任务，并具有值得评估的支持。