BIGDATA: Collaborative Research: F: From Data Geometries to Information Networks

BIGDATA:协作研究:F:从数据几何到信息网络

基本信息

  • 批准号:
    1546392
  • 负责人:
  • 金额:
    $ 50万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-01-01 至 2017-05-31
  • 项目状态:
    已结题

项目摘要

Big Data often results from multiple sources, giving collections that contain multiple, often partial, "views" of the same object, space, or phenomenon from various observers. Extracting information robustly from such data sets calls for a joint analysis of a large collection of data sets. The project is developing a novel geometric framework for modeling, structure detection, and information extraction from a collection of large related data sets, with an emphasis on the relationships between data. While this approach clearly applies to data with a clear geometric character (e.g., objects in images), the work is also applied to datasets as diverse as computer networks (identifying common structure in subnets) and Massive Open Online Course homework data (automatically carrying grader annotations to similar problems in other students' homeworks).The novel framework is based on the construction of maps between the objects under considerations (point clouds, graphs, images, etc...), and on the analysis of the networks of maps that result as a way of extracting information, generating latent models for the data, and transporting or inferring functional / semantic information. These tasks define a new field of map processing between data sets and require tool sets with new ideas from functional analysis, non-convex optimization, and homological algebra in mathematics, and geometric algorithms, machine learning, optimization, and approximation algorithms in computer science. Sophisticated algorithmic techniques for attacking the large-scale non-linear optimization problems that emerge within the framework will also be investigated.
大数据通常来自多个来源,给出的集合包含来自不同观察者的同一对象、空间或现象的多个、往往是部分的“观点”。从这样的数据集中稳健地提取信息需要对大量数据集进行联合分析。该项目正在开发一种新的几何框架,用于从大型相关数据集的集合中进行建模、结构检测和信息提取,重点是数据之间的关系。虽然这种方法显然适用于具有明确几何特征的数据(例如,图像中的对象),但该工作也适用于各种数据集,如计算机网络(识别子网络中的共同结构)和海量开放在线课程作业数据(自动携带对其他学生家庭作业中类似问题的评分器注释)。该框架基于考虑对象(点云、图形、图像等)之间的地图构建,以及对地图网络的分析,作为提取信息、为数据生成潜在模型以及传输或推断功能/语义信息的一种方式。这些任务定义了数据集之间地图处理的新领域,并需要具有新思想的工具集,这些工具集来自数学中的泛函分析、非凸优化和同调代数,以及计算机科学中的几何算法、机器学习、优化和逼近算法。还将研究用于解决框架内出现的大规模非线性优化问题的复杂算法技术。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data
  • DOI:
  • 发表时间:
    2016-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wenjing Liao;M. Maggioni
  • 通讯作者:
    Wenjing Liao;M. Maggioni
Spectral–Spatial Diffusion Geometry for Hyperspectral Image Clustering
Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion
非线性扩散高光谱图像的无监督聚类和主动学习
Unsupervised Discriminative Dimension Reduction for Hyperspectral Chemical Plume Segmentation. International Geoscience and Remote Sensing Symposium (IGARSS)
高光谱化学羽流分割的无监督判别维数缩减。
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Murphy, James M.;Maggioni, Mauro
  • 通讯作者:
    Maggioni, Mauro
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Mauro Maggioni其他文献

A scalable framework for learning the geometry-dependent solution operators of partial differential equations
用于学习偏微分方程的几何依赖解算符的可扩展框架
  • DOI:
    10.1038/s43588-024-00732-2
  • 发表时间:
    2024-12-09
  • 期刊:
  • 影响因子:
    18.300
  • 作者:
    Minglang Yin;Nicolas Charon;Ryan Brody;Lu Lu;Natalia Trayanova;Mauro Maggioni
  • 通讯作者:
    Mauro Maggioni
Critical Exponent of Short Even Filters andBurt-Adelson Biorthogonal Wavelets
  • DOI:
    10.1007/s006050070024
  • 发表时间:
    2000-11-15
  • 期刊:
  • 影响因子:
    0.800
  • 作者:
    Mauro Maggioni
  • 通讯作者:
    Mauro Maggioni
DH-482888-001 PREDICTING PERSONALIZED CARDIAC ELECTROPHYSIOLOGY USING DEEP LEARNING
DH-482888-001 使用深度学习预测个性化心脏电生理学
  • DOI:
    10.1016/j.hrthm.2024.03.261
  • 发表时间:
    2024-05-01
  • 期刊:
  • 影响因子:
    5.700
  • 作者:
    Minglang Yin;Nicolas Charon;Ryan Brody;Lu Lu;Mauro Maggioni;Natalia A. Trayanova
  • 通讯作者:
    Natalia A. Trayanova
PO-01-212 strongA NOVEL DEEP LEARNING MODEL FOR PATIENT-SPECIFIC COMPUTATIONAL MODELING OF CARDIAC ELECTROPHYSIOLOGY/strong
PO-01-212 一种用于患者特异性心脏电生理计算建模的强大新型深度学习模型
  • DOI:
    10.1016/j.hrthm.2023.03.530
  • 发表时间:
    2023-05-01
  • 期刊:
  • 影响因子:
    5.700
  • 作者:
    Minglang Yin;Lu Lu;Mauro Maggioni;Natalia A. Trayanova
  • 通讯作者:
    Natalia A. Trayanova

Mauro Maggioni的其他文献

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{{ truncateString('Mauro Maggioni', 18)}}的其他基金

BIGDATA: F: Compositional Learning, Maps and Transfer: Statistical and Machine Learning on Collections of Data Sets
BIGDATA:F:组合学习、地图和迁移:数据集集合的统计和机器学习
  • 批准号:
    1837991
  • 财政年份:
    2019
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
ATD: Estimation and Anomaly Detection for high-dimensional Data, Maps and Dynamic Processes
ATD:高维数据、地图和动态过程的估计和异常检测
  • 批准号:
    1737984
  • 财政年份:
    2017
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
ATD: Online Multiscale Algorithms for Geometric Density Estimation in High-Dimensions and Persistent Homology of Data for Improved Threat Detection
ATD:用于高维几何密度估计和数据持久同源性的在线多尺度算法,以改进威胁检测
  • 批准号:
    1756892
  • 财政年份:
    2016
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Proposal: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis
合作提案:SI2-CHE:用于高级采样和分析的 ExTASY 可扩展工具
  • 批准号:
    1708353
  • 财政年份:
    2016
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
BIGDATA: Collaborative Research: F: From Data Geometries to Information Networks
BIGDATA:协作研究:F:从数据几何到信息网络
  • 批准号:
    1708553
  • 财政年份:
    2016
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Statistical Learning for High-Dimensional Stochastic Dynamical Systems
高维随机动力系统的统计学习
  • 批准号:
    1708602
  • 财政年份:
    2016
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant
Structured Dictionary Models and Learning for High Resolution Images
高分辨率图像的结构化字典模型和学习
  • 批准号:
    1724979
  • 财政年份:
    2016
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Statistical Learning for High-Dimensional Stochastic Dynamical Systems
高维随机动力系统的统计学习
  • 批准号:
    1522651
  • 财政年份:
    2015
  • 资助金额:
    $ 50万
  • 项目类别:
    Continuing Grant
Structured Dictionary Models and Learning for High Resolution Images
高分辨率图像的结构化字典模型和学习
  • 批准号:
    1320655
  • 财政年份:
    2013
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Proposal: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis
合作提案:SI2-CHE:用于高级采样和分析的 ExTASY 可扩展工具
  • 批准号:
    1265920
  • 财政年份:
    2013
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant

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