Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many real-world applications, the data exists in the form of attributed sequences, which is composed of a set of fixed-size attributes and variable-length sequences with dependencies between them. In the attributed sequence context, feature learning remains challenging due to the dependencies between sequences and their associated attributes. In this dissertation, we focus on analyzing and building deep learning models for four new problems on attributed sequences. Our extensive experiments on real-world datasets demonstrate that the proposed solutions significantly improve the performance of each task over the state-of-the-art methods on attributed sequences.
翻译:最近对特征学习的研究已扩大到序列数据,其中每个实例都包含一系列不同项目,其长度可变。然而,在许多现实世界应用中,数据以归属序列的形式存在,该序列由一系列固定大小的属性和不同长度的序列组成,它们彼此依存。在归属序列背景下,特征学习仍然具有挑战性,因为序列及其相关属性之间的依赖性。在本次论文中,我们侧重于分析和建立深层次学习模型,解决四个关于归属序列的新问题。我们对真实世界数据集的广泛实验表明,拟议的解决方案大大改进了每项任务在归属序列方面最先进的方法上的绩效。