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

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。

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.