ATD: Estimation and Anomaly Detection for high-dimensional Data, Maps and Dynamic Processes

该项目侧重于常规、高光谱、激光雷达和热图像以及多模态数据集的分析，包括异常检测和分类任务。许多威胁检测问题的输入数据（如图像、光谱等）通常是高维的，受到噪声的破坏，并且由于环境条件而受到非线性变换。自动威胁检测问题通常面临维度的基本诅咒：为了达到目标级别的准确性，所需的观测数量在数据的维度上呈指数级增长。这项工作的重点是自动发现数据的低维表示，或者至少是那些足以执行手头任务的数据特征。即使使用相对较少的数据量，这些表示也将在上述任务中实现较高的统计和计算性能。该项目还将关注交互代理系统中的自动建模和学习交互规则。该项目需要一个总体的研究计划，旨在检测和利用固有的低维，并估计数据和某些类型的高维数据和基于代理的系统的低维模型。将构建由高光谱成像（HSI）、激光雷达和近红外/夜视相机产生的高维数据的低维概率模型，实现高效的数据编码和解码，用于检测背景噪声与感兴趣信号的统计模型，以及异常检测。通过研究不同传感器收集的数据之间的高维地图来理解多个传感器模式之间的依赖关系的新技术将在各种多模态数据集上开发和测试。从具有未知影响函数的智能体系统中学习的新型机器学习技术将得到发展。

The project focuses on the analysis of collections of regular, hyperspectral, LiDAR, and thermal images, and multi-modal data sets, both in terms of detection of anomalies and classification tasks. The input data (such as images, spectra, etc.) for many threat detection problems is typically high-dimensional, corrupted by noise, and subject to nonlinear transformations due to environmental conditions. Automatic threat detection problems typically face the fundamental curse of dimensionality: to achieve a target level of accuracy, the number of observations required is exponential in the dimension of the data. This work focuses on the automated discovery of low-dimensional representations of the data, or at least of those features of the data that are sufficient to perform the task at hand. These representations will enable high statistical and computational performance in the above tasks even with a relatively small amount of data. The project will also focus on automatically modeling and learning interaction rules in interacting agent systems.This project entails an overarching program of research aimed at detecting and exploiting intrinsic low-dimensionality and estimating low-dimensional models for data and certain types of high-dimensional data and agent-based systems. Low-dimensional probabilistic models for high-dimensional data, arising from Hyper-Spectral Imaging (HSI), LiDAR, and Near-Infrad/Night-Vision cameras, will be constructed, enabling efficient data encoding and decoding, statistical models for detecting background noise versus signals of interest, and anomaly detection. Novel techniques for understanding dependencies across multiple sensor modalities by studying maps in high-dimensions between data collected by different sensors will be developed and tested on a variety of multi-modal data sets. Novel machine learning techniques for learning from agent systems with unknown influence functions will be developed.