CAREER: New Change-Point Problems in Analyzing High-Dimensional and Non-Euclidean Data
职业:分析高维和非欧几里得数据的新变点问题
基本信息
- 批准号:1848579
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-07-01 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In this big data era, massive data sequences are collected in various scientific fields for studying complicated phenomena over time and space, including neuroscience, epidemiology, social science, computer vision, and astronomy. Change-point analysis is a crucial early step in analyzing these data sequences, such as, to raise an alarm when an abnormal event happens in online data monitoring, and to segment a long sequence into more homogeneous parts for follow-up studies. To accommodate modern applications, the ability to deal with high throughput data and data with complicated structures is becoming a necessity. Parametric methods usually cannot be applied to very high dimensions unless strong assumptions are made to avoid the estimation of a large number of nuisance parameters. This project focuses on developing non-parametric change-point detection methods that are free of strong assumptions and computationally scalable to high dimensional and complex data. This project provides students and researchers with exciting new research problems that have both statistical and scientific importance. The training component for undergraduate and graduate students will prepare new researchers with inter-disciplinary education.This project will develop a new scan statistic framework through a novel adaptation of graph-based methods. The PI has shown that the graph-based approaches scale to high-dimensional and non-Euclidean data, and allow for universal analytic permutation p-value approximations that is decoupled from application-specific modeling, facilitating their applications to large and complicated data sets. Despite the good properties of the graph-based methods, there are still some gaps between its current versions and many modern applications. This project aims to fill those important gaps. In particular, this project will (1) develop new graph-based approaches to effectively integrate information from multiple sources, which is common in many application areas, such as smart homes and smart cities, and seek ways to distribute the new approaches to local centers to avoid the excessive transmission of raw data in a distributed system; (2) develop treatments from the level of constructing the graph to deal with dependent data, which is more effective than a circular block permutation framework developed by the PI earlier; and (3) develop a new framework to provide analytic power approximations to the graph-based methods that kick in for sample sizes in hundreds and thousands even for high-dimensional data and non-Euclidean data, facilitating researchers to make better decisions in real applications. These methodological and theoretical developments will provide better understandings of modern complicated data sequences from diverse fields, which will further advance the understanding of major scientific problems in these fields. The tools developed in this project will be distributed as open source software packages with detailed documentations. This will enhance the collaboration between the statistics community and researchers from broader scientific fields, and make data analysis procedures more transparent.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.
在这个大数据时代,大量数据序列是在各种科学领域收集的,用于研究随着时间和空间的复杂现象,包括神经科学,流行病学,社会科学,计算机视觉和天文学。 变更点分析是分析这些数据序列的关键早期一步,例如在在线数据监视中发生异常事件时引起警报,并将长序列分为更均匀的部分进行后续研究。 为了适应现代应用,处理具有复杂结构的高吞吐量数据和数据的能力已成为必要。 除非做出强大的假设以避免估计大量的滋扰参数,否则通常不能将参数方法应用于非常高的维度。 该项目的重点是开发非参数更改点检测方法,这些方法没有强有力的假设,并且在高维和复杂数据上可扩展到计算上。 该项目为学生和研究人员提供了令人兴奋的新研究问题,这些问题具有统计和科学的重要性。 本科生和研究生的培训组成部分将为新的研究人员提供跨学科教育的准备。该项目将通过新颖的基于图的方法来开发新的扫描统计框架。 PI表明,基于图的方法将量表用于高维和非欧亚人数据,并允许通用分析置换式p值近似值,这些近似值与应用程序特定的建模分离,从而促进了它们在大型且复杂的数据集中的应用。 尽管基于图的方法具有良好的属性,但其当前版本与许多现代应用程序之间仍然存在一些差距。该项目旨在填补那些重要的空白。 特别是,该项目将(1)开发新的基于图的方法来有效整合来自多个来源的信息,这些信息在许多应用领域(例如智能家居和智能城市)中很常见,并寻求将新方法分配给本地中心的方法避免在分布式系统中过度传输原始数据; (2)开发从构造图的水平开发处理以处理相关数据的处理,这比PI早期开发的圆形块置换框架更有效; (3)开发一个新框架,以提供基于图的方法的分析能力近似值,该方法为数百万的样本量即使在高维数据和非欧几里得数据方面为数百种,促进研究人员在实际应用中做出更好的决策。 这些方法论和理论发展将提供对来自不同领域的现代复杂数据序列的更好理解,这将进一步促进对这些领域的主要科学问题的理解。 该项目中开发的工具将作为带有详细文档的开源软件包分发。 这将增强统计社区与更广泛的科学领域的研究人员之间的合作,并使数据分析程序更加透明。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的智力优点和更广泛的影响审查标准的评估来获得支持的。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Normality Test for High-dimensional Data Based on the Nearest Neighbor Approach
- DOI:10.1080/01621459.2021.1953507
- 发表时间:2019-04
- 期刊:
- 影响因子:3.7
- 作者:Hao Chen;Yin Xia
- 通讯作者:Hao Chen;Yin Xia
A Fast and Efficient Change-Point Detection Framework Based on Approximate $k$-Nearest Neighbor Graphs
一种基于近似$k$-最近邻图的快速高效的变化点检测框架
- DOI:10.1109/tsp.2022.3162120
- 发表时间:2022
- 期刊:
- 影响因子:5.4
- 作者:Liu, Yi-Wei;Chen, Hao
- 通讯作者:Chen, Hao
Likelihood Scores for Sparse Signal and Change-Point Detection
稀疏信号和变化点检测的似然评分
- DOI:10.1109/tit.2023.3242297
- 发表时间:2023
- 期刊:
- 影响因子:2.5
- 作者:Hu, Shouri;Huang, Jingyan;Chen, Hao;Chan, Hock Peng
- 通讯作者:Chan, Hock Peng
Sequential Change-Point Detection for High-Dimensional and Non-Euclidean Data
高维和非欧几里德数据的顺序变化点检测
- DOI:10.1109/tsp.2022.3205763
- 发表时间:2022
- 期刊:
- 影响因子:5.4
- 作者:Chu, Lynna;Chen, Hao
- 通讯作者:Chen, Hao
Graph-Based Change-Point Analysis
- DOI:10.1146/annurev-statistics-122121-033817
- 发表时间:2023-03
- 期刊:
- 影响因子:7.9
- 作者:Hao Chen;Lynna Chu
- 通讯作者:Hao Chen;Lynna Chu
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Hao Chen其他文献
Intrusion Detection: Characterising intrusion detection sensors
入侵检测:表征入侵检测传感器
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
S. Shaikh;Howard Chivers;P. Nobles;John A. Clark;Hao Chen - 通讯作者:
Hao Chen
Attacks on Search RLWE
对搜索 RLWE 的攻击
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Hao Chen;K. Lauter;Katherine E. Stange - 通讯作者:
Katherine E. Stange
Conversion from Intermediate Age-Related Macular Degeneration to Geographic Atrophy in a Proxima B Subcohort Using a Multimodal Approach
使用多模式方法将 Proxima B 亚组中的中度年龄相关性黄斑变性转化为地理萎缩
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:2.6
- 作者:
S. Schmitz;Martina D. Braun;S. Thiele;Daniela Ferrara;L. Honigberg;Simon S. Gao;Hao Chen;Verena Steffen;F. Holz;M. Sassmannshausen - 通讯作者:
M. Sassmannshausen
Does he ping promote we-being in at-risk youth and ex-ofender samp es ?
他平是否能促进高危青少年和刑满释放人员样本中的我们的幸福感?
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Xijing Wang;Zhansheng Chen;Eva G. Krumhuber;Hao Chen - 通讯作者:
Hao Chen
HOW FAR AND HOW FAST CAN ONE MOVE ON NEUTRAL NETWORK
一个人可以在中性网络上移动多远和多快
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Chenghang Du;Hao Chen;Yunjie Zhao;Chen Zeng - 通讯作者:
Chen Zeng
Hao Chen的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Hao Chen', 18)}}的其他基金
ERI: Representations of Complex Engineering Systems via Technology Recursion and Renormalization Group
ERI:通过技术递归和重整化群表示复杂工程系统
- 批准号:
2301627 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Making Use of the Curse of Dimensionality in Modern Data Analysis
在现代数据分析中利用维度诅咒
- 批准号:
2311399 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Development of Absolute Quantitative Protein Footprinting Mass Spectrometry (aqPFMS) for Probing Protein 3D Structures
开发用于探测蛋白质 3D 结构的绝对定量蛋白质足迹质谱 (aqPFMS)
- 批准号:
2203284 - 财政年份:2022
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
SaTC: CORE: Small: Collaborative: Understanding and Detecting Memory Bugs in Rust
SaTC:核心:小:协作:理解和检测 Rust 中的内存错误
- 批准号:
1956364 - 财政年份:2020
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
SaTC: CORE: Medium: Collaborative: Towards Robust Machine Learning Systems
SaTC:核心:媒介:协作:迈向稳健的机器学习系统
- 批准号:
1801751 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
Development of Electrochemical Mass Spectrometry for the Study of Protein Redox Chemistry and Protein Structures
用于研究蛋白质氧化还原化学和蛋白质结构的电化学质谱法的发展
- 批准号:
1915878 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
Development of Electrochemical Mass Spectrometry for the Study of Protein Redox Chemistry and Protein Structures
用于研究蛋白质氧化还原化学和蛋白质结构的电化学质谱法的发展
- 批准号:
1709075 - 财政年份:2017
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
Change-Point Analysis for Multivariate and Object Data
多变量和对象数据的变点分析
- 批准号:
1513653 - 财政年份:2015
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
CAREER: Development of Microsecond Time-Resolved Mass Spectrometry for the Study of Biochemical Reaction Mechanisms and Kinetics
职业:开发微秒时间分辨质谱用于生化反应机制和动力学研究
- 批准号:
1149367 - 财政年份:2012
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
TC: Small: Designing New Authentication Mechanisms using Hardware Capabilities in Advanced Mobile Devices
TC:小型:使用高级移动设备中的硬件功能设计新的身份验证机制
- 批准号:
1018964 - 财政年份:2010
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
相似国自然基金
基于多模态特征对新辅助化疗中胃癌患者心血管功能改变及疗效的动态评估
- 批准号:
- 批准年份:2022
- 资助金额:55 万元
- 项目类别:面上项目
R(D(*))和R(K(*))反常的新物理效应及top夸克味改变耦合的对撞机唯象研究
- 批准号:11905096
- 批准年份:2019
- 资助金额:19.0 万元
- 项目类别:青年科学基金项目
细胞色素P450酶2D6(CYP2D6)新变异体功能改变的分子机理与临床表型研究
- 批准号:81973397
- 批准年份:2019
- 资助金额:55 万元
- 项目类别:面上项目
多重行为改变理论指导下的新媒体戒烟干预研究
- 批准号:71573047
- 批准年份:2015
- 资助金额:47.0 万元
- 项目类别:面上项目
重味夸克衰变中的标准模型检验与新物理探寻
- 批准号:11575151
- 批准年份:2015
- 资助金额:60.0 万元
- 项目类别:面上项目
相似海外基金
Dlgap2 as a Regulator of Alzheimer's Disease Related Cognitive Declines Via Synaptic Modifications
Dlgap2 通过突触修饰调节阿尔茨海默病相关的认知下降
- 批准号:
10606051 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
Air Pollution, Multidimensional Behavior, and Neuroimaging in Children with Neurodevelopmental Disorders
空气污染、多维行为和神经发育障碍儿童的神经影像学
- 批准号:
10644622 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
GRC: One Health Approaches to Urbanization, Water, and Food Security
GRC:城市化、水和粮食安全的同一个健康方法
- 批准号:
10753642 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
The ANTIDOTE Institute- Advancing New Toxicology Investigators in Drug abuse and Original Translational research Efforts
ANTIDOTE Institute - 推动新毒理学研究人员在药物滥用和原创转化研究工作中的发展
- 批准号:
10681927 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别: