Attributed Sequence Embedding

Abstract

Mining tasks over sequential data, such as clickstreams and gene sequences, require a careful design of embeddings usable by learning algorithms. Recent research in feature learning has been extended to sequential data, where each instance consists of a sequence of heterogeneous items with a variable length. However, many real-world applications often involve attributed sequences, where each instance is composed of both a sequence of categorical items and a set of attributes. In this paper, we study this new problem of attributed sequence embedding, where the goal is to learn the representations of attributed sequences in an unsupervised fashion. This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences. This problem is challenging due to the dependencies between sequences and their associated attributes. We propose a deep multimodal learning framework, called NAS, to produce embeddings of attributed sequences. The embeddings are task independent and can be used on various mining tasks of attributed sequences. We demonstrate the effectiveness of our embeddings of attributed sequences in various unsupervised learning tasks on real-world datasets.

arXiv commentAccepted by IEEE Big Data 2019

References in corpus (6)

• Sequence to Sequence Learning with Neural Networks
• DeepWalk: Online Learning of Social Representations
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
• A Subsequence Interleaving Model for Sequential Pattern Mining
• One-Shot Learning on Attributed Sequences

Cited by in corpus (4)

• CoLES: Contrastive Learning for Event Sequences with Self-Supervision
• FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
• Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection
• One-Shot Learning on Attributed Sequences