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Method and system for determining an optimal or near optimal set of contexts by constructing a multi-directional context tree

机译:通过构造多方向上下文树确定最佳或接近最佳上下文集的方法和系统

摘要

In various embodiments of the present invention, optimal or near-optimal multidirectional context sets for a particular data-and/or-signal analysis or processing task are determined by selecting a maximum context size, generating a set of leaf nodes corresponding to those maximally sized contexts that occur in the data or signal to be processed or analyzed, and then building up and concurrently pruning, level by level, a multidirectional optimal context tree constructing one of potentially many optimal or near-optimal context trees in which leaf nodes represent the context of a near-optimal or optimal context set that may contain contexts of different sizes and geometries. Pruning is carried out using a problem-domain-related weighting function applicable to nodes and subtrees within the context tree. In one described embodiment, a bi-directional context tree suitable for a signal denoising application is constructed using, as the weighting function, an estimated loss function.
机译:在本发明的各种实施例中,通过选择最大上下文尺寸,生成与最大尺寸的叶节点相对应的叶节点的集合,来确定用于特定数据和/或信号分析或处理任务的最优或接近最优的多方向上下文集。在要处理或分析的数据或信号中出现的上下文,然后逐级构建并同时修剪多方向的最佳上下文树,该树构造了潜在的许多最佳或接近最佳的上下文树之一,其中叶节点表示上下文可能包含不同大小和几何形状的上下文的近乎最佳或最佳的上下文集。使用适用于上下文树中的节点和子树的问题域相关加权函数来执行修剪。在一个所描述的实施例中,使用估计的损失函数作为加权函数来构造适合于信号去噪应用的双向上下文树。

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