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

• 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

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.

项目成果

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)
• 作者: Song Han;Tao Gong;M. Nixon;Eric Rotvold;K. Lam;K. Ramamritham

Discrete Graph Structure Learning for Forecasting Multiple Time Series

• 发表时间: 2021-01
• 期刊: ArXiv
• 作者: Chao Shang;Jie Chen;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.
• 作者: Guannan Liang;Qianqian Tong;Jiahao Ding;Miao Pan;J. Bi

Improved Dynamic Graph Learning through Fault-Tolerant Sparsification

• 发表时间: 2019-05
• 期刊: Proceedings of machine learning research
• 作者: Chun Jiang Zhu;Sabine Storandt;K. Lam;Song Han;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
• 作者: Shang C;Palmer A;Sun J;Chen KS;Lu J;Bi J
• 通讯作者: Bi J