Principal Curve Clustering With Noise

Clustering on principal curves combines parametric modeling of noise with nonparametric modeling of feature shape This is useful for detecting curvilinear features in spatial point patterns with or without background noise Applications of this include the detection of curvilinear mine elds from reconnaissance images some of the points in which represent false detections and the detection of seismic faults from earthquake catalogs Our algorithm for principal curve clustering is in two steps the rst is hierarchical and agglomerative HPCC and the second consists of iterative relocation based on the Clas si cation EM algorithm CEM PCC HPCC is used to combine potential feature clusters while CEM PCC re nes the results and deals with background noise It is important to have a good starting point for the algorithm this can be found manually or automatically using for example nearest neighbor clutter removal or model based clustering We choose the number of features and the amount of smoothing simultaneously using approximate Bayes factors

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