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RI: Small: Multi-View Latent Class Discovery and Prediction with a Streamlined Analytics Platform

RI：小型：使用简化的分析平台进行多视图潜在类别发现和预测

基本信息

批准号：
1718738
负责人：
Jinbo Bi
金额：
$ 45万
依托单位：
University of Connecticut
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718738&HistoricalAwards=false
关键词：
RI Small Multi View Latent

项目摘要

Discovering latent subgroups in a sample is an important problem in many scientific disciplines. Social scientists identify subgroups within a population based on behavioral patterns to examine differential effects of social status. Engineers recognize malfunctions of a manufacturing system based on performance measures to detect design defects. Physicians define subtypes of a disorder on the basis of clinical symptoms to identify associated genetic risk factors. This kind of problem involves two sets of variables: a set of descriptors describing the issue (e.g., behavioral patterns, or symptoms) and a set of moderators or predictors (e.g., social status, or genetic factors). The ability to accurately predict the latent classes (e.g., disease subtypes) from predictors (e.g., genetic risk) in the absence of observed descriptors (e.g., before symptoms are developed) will advance many of these disciplines. This project aims to develop an effective and efficient platform of machine learning algorithms to solve this problem. The team will effectively integrate research and teaching to engage students into the proposed study. Validated methods and software will be broadly disseminated through the project web repository and scientific presentations.This project addresses the latent class discovery and prediction problem by deriving novel and efficient approaches, including multi-view co-clustering, multi-view subspace clustering, multi-objective optimization of co-training, and multi-modal deep learning methods. Parallel and distributed algorithms will be developed to implement and scale up these methods. A streamlined analytics platform will be constructed to maximize the utility of the proposed approaches in real-world applications. The proposed solutions will be evaluated in the analysis of large-scale sensory and behavioral data. By collaborating with domain experts, the project will (1) identify risk factors for problematic human behaviors such as binge drinking; and (2) locate the sensory features most discriminative of gait abnormalities due to neurological disorders such as Parkinson's disease or stroke.

发现样本中的潜在子群是许多科学学科中的一个重要问题。社会科学家根据行为模式在人群中确定子群体，以检查社会地位的差异影响。工程师根据性能测量识别制造系统的故障以检测设计缺陷。医生根据临床症状定义疾病的亚型，以确定相关的遗传风险因素。这类问题涉及两组变量：一组描述问题的描述符（例如，行为模式或症状）和一组调节器或预测器（例如，社会地位或遗传因素）。准确预测潜在类的能力（例如，疾病亚型）与预测因子（例如，遗传风险）在没有观察到的描述符（例如，在症状出现之前）将推动许多这些学科的发展。该项目旨在开发一个有效和高效的机器学习算法平台来解决这个问题。该团队将有效地整合研究和教学，让学生参与拟议的研究。经过验证的方法和软件将通过项目网络存储库和科学演示广泛传播。该项目通过衍生新颖有效的方法来解决潜在类别发现和预测问题，包括多视图联合聚类，多视图子空间聚类，多目标优化联合训练和多模态深度学习方法。将开发并行和分布式算法来实施和扩大这些方法。将构建一个精简的分析平台，以最大限度地提高所提出的方法在现实世界应用中的效用。所提出的解决方案将在大规模的感官和行为数据的分析中进行评估。通过与领域专家合作，该项目将（1）确定有问题的人类行为的风险因素，如酗酒;（2）定位由于神经系统疾病（如帕金森病或中风）导致的步态异常的最具鉴别力的感觉特征。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

RT-DAP: A Real-Time Data Analytics Platform for Large-Scale Industrial Process Monitoring and Control

DOI：
10.1109/icii.2018.00015
发表时间：
2018-02
期刊：
2018 IEEE International Conference on Industrial Internet (ICII)
影响因子：
0
作者：
Song Han;Tao Gong;M. Nixon;Eric Rotvold;K. Lam;K. Ramamritham
通讯作者：
Song Han;Tao Gong;M. Nixon;Eric Rotvold;K. Lam;K. Ramamritham

Discrete Graph Structure Learning for Forecasting Multiple Time Series

DOI：
发表时间：
2021-01
期刊：
ArXiv
影响因子：
0
作者：
Chao Shang;Jie Chen;J. Bi
通讯作者：
Chao Shang;Jie Chen;J. Bi

Improved Dynamic Graph Learning through Fault-Tolerant Sparsification

DOI：
发表时间：
2019-05
期刊：
Proceedings of machine learning research
影响因子：
0
作者：
Chun Jiang Zhu;Sabine Storandt;K. Lam;Song Han;J. Bi
通讯作者：
Chun Jiang Zhu;Sabine Storandt;K. Lam;Song Han;J. Bi

Stochastic privacy-preserving methods for nonconvex sparse learning

DOI：
10.1016/j.ins.2022.09.062
发表时间：
2022-10
期刊：
Inf. Sci.
影响因子：
0
作者：
Guannan Liang;Qianqian Tong;Jiahao Ding;Miao Pan;J. Bi
通讯作者：
Guannan Liang;Qianqian Tong;Jiahao Ding;Miao Pan;J. Bi

VIGAN: Missing View Imputation with Generative Adversarial Networks.

DOI：
10.1109/bigdata.2017.8257992
发表时间：
2017
期刊：
Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data
影响因子：
0
作者：
Shang C;Palmer A;Sun J;Chen KS;Lu J;Bi J
通讯作者：
Bi J