Collaborative Research: CDS&E-MSS: Robust Algorithms for Interpolation and Extrapolation in Manifold Learning

合作研究：CDS

• 批准号: 1317424
• 负责人: Qiang Ye
• 金额: $ 13.99万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317424&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS&E; MSS; Robust

The objective of this proposal is to develop robust algorithms for reconstructing or synthesizing highly structured high-dimensional data based on a low-dimensional representation learned from a training dataset, i.e., the interpolation and extrapolation problems in manifold learning. The project will address the elusive issue of computing a usually not well-defined low-dimensional parametrization in the setting of various interpolation and extrapolation problems for manifold learning, emphasizing the notion of physically meaningful paramterizations. It will develop innovative computational methodology for flexibly learning a low-dimensional parametrization together with other physically important variables in the context of both unsupervised and semi-supervised learning and especially active learning settings, for learning and synthesis of dynamic data, and for manifold extrapolation based on transfer learning. Included in the project is a development of a publicly available software package which will disseminate the research results and promote applications of nonlinear dimension reduction methodology to real-world problems.The discoveries from this proposed research are expected to impact a wide range of areas of applications. Computing compact representation of high-dimensional data represents a very challenging statistical learning problem, and manifold learning has become a very active research field aiming at discovering hidden structures from the statistical and geometric regularity inherent in many high-dimensional data. Reconstruction and synthesis of high-dimensional data in the context of interpolation and extrapolation will have significant applications in image and video processing, computer vision, video surveillance for homeland security, computational biology, and scientific visualization. The proposed theoretical tools and computational methods have the promise of significantly expanding the applicability and functionality of existing and new manifold learning methods and thus advancing the state of the art in nonlinear dimension reduction research. The proposed research lies at the interface between applied mathematics, computational science, and machine learning applications and provides an ideal setting for research cross-fertilization and collaboration as well as training of graduate students in interdisciplinary research.

该提议的目的是开发用于基于从训练数据集学习的低维表示来重建或合成高度结构化的高维数据的鲁棒算法，即，流形学习中的内插和外推问题。该项目将解决在流形学习的各种内插和外推问题的设置中计算通常定义不明确的低维参数化的难以捉摸的问题，强调物理意义参数化的概念。它将开发创新的计算方法，用于在无监督和半监督学习以及特别是主动学习设置的背景下灵活地学习低维参数化以及其他物理上重要的变量，用于动态数据的学习和合成，以及基于迁移学习的流形外推。该项目包括开发一个可公开获得的软件包，该软件包将传播研究成果，并促进非线性降维方法在现实世界问题中的应用。预计这项拟议研究的发现将影响广泛的应用领域。计算高维数据的紧凑表示是一个非常具有挑战性的统计学习问题，流形学习已经成为一个非常活跃的研究领域，旨在从许多高维数据固有的统计和几何规律中发现隐藏的结构。在内插和外推的背景下重建和合成高维数据将在图像和视频处理、计算机视觉、国土安全视频监控、计算生物学和科学可视化中具有重要应用。所提出的理论工具和计算方法有希望显着扩展现有的和新的流形学习方法的适用性和功能，从而推进非线性降维研究的最新技术水平。拟议的研究位于应用数学，计算科学和机器学习应用之间的接口，并为研究交叉施肥和合作以及跨学科研究的研究生培训提供了理想的环境。