Efficient Condensation of Spatial/Temporal Data

9971784拟议的研究涉及为分类和异常检测的目的对空间/时间数据进行压缩和分析。重点研究了基于空间点过程的地标估计和空间聚类的新方法。这样的过程提供了一类潜在的丰富的模型来表达关于曲线和形状的高级先验知识。这些模型比常用的离散马尔可夫随机场模型和模板变形模型更适合，特别是在缺乏训练数据的情况下。使用新方法将开发一种版本的贝叶斯非参数曲线估计。主要目标是实现地标信息的简明规范(例如，描述样条线中节点的位置)。另一个目标是改进从时态数据进行同时推理的方法。高效统计模型的好处是数据浓缩，即在不牺牲关键统计信息的情况下，将经常难以管理的大型数据集减少到简约的形式。另一个好处是，它可以导致发现有趣的异常现象，因为有合适的推理方法来证明这些发现是可信的。有待探索的具体应用包括离线签名识别和2D-Gel凝胶成像。

9971784The proposed research concerns the condensation and analysis of spatial/temporal data for purposes of classification and anomaly detection. The focus is a new approach to landmark estimation and spatial clustering based on spatial point processes. Such processes provide a potentially rich class of models to express high-level prior knowledge about curves and shapes. These models are more suitable than commonly used discrete Markov random field models and template deformation models, especially in situations where there is a lack of training data. A version of Bayesian nonparametric curve estimation will be developed using the new approach. A principal objective is to achieve a parsimonious specification of the landmark information (describing the location of the knots in a spline curve for example). A further objective is improved methods for simultaneous inference from temporal data.The benefit of an efficient statistical model is data condensation, or the reduction of often unmanageably large data sets to a parsimonious form, without the sacrifice of key statistical information. Another benefit is that it can lead to the discovery of interesting anomalies, given the availability of suitable inferential methods to lend credence to such findings. Specific applications to be explored include off-line signature recognition and 2D-gel electrophoresis imaging.