序列图变换（SGT）：一种用于序列数据挖掘的特征嵌入函数
Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining
摘要：
由于序列的非结构性，序列特征嵌入是一项具有挑战性的任务，即，任意长度的任意字符串。现有的方法在提取短期依赖关系方面是有效的，但通常遭受长期的计算问题。序列图变换（SGT），一个特征嵌入函数，可以提取不同数量的短期到长期的依赖关系，而不增加计算。SGT的属性分析证明解释下正常和均匀分布的假设。与现有方法（包括最先进的序列/字符串内核和LSTM）相比，SGT特征在序列聚类和分类方面具有更高的准确性和更低的计算量。
英文摘要：
Sequence feature embedding is a challenging task due to the unstructuredness of sequence, i.e., arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT's properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
