Efficient Condensation of Spatial/Temporal Data

9971784提出的研究涉及空间/时间数据的浓缩和分析，目的是分类和异常检测。重点是一种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个潜在的丰富的模型类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。一个版本的贝叶斯非参数曲线估计将开发使用新的方法。一个主要目标是实现标记信息的简洁规范（例如描述样条曲线中节点的位置）。进一步的目标是改进从时间数据同时推断的方法。高效统计模型的好处是数据浓缩，或者将通常难以管理的大型数据集简化为简洁的形式，而不会牺牲关键的统计信息。另一个好处是，它可以导致发现有趣的异常，如果有合适的推理方法来证明这些发现。具体应用包括离线签名识别和2d凝胶电泳成像。

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.