Deep Learning on Attributed Sequences
This work addresses the problem of analyzing complex attributed sequence data for researchers and practitioners in machine learning, though it appears incremental as it builds on existing sequence feature learning.
The dissertation tackled the challenge of feature learning for attributed sequences, which combine fixed-size attributes and variable-length sequences with dependencies, by developing deep learning models for four new problems and demonstrated significant performance improvements over state-of-the-art methods on real-world datasets.
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.