MLAS：属性序列的度量学习
MLAS: Metric Learning on Attributed Sequences
摘要：
距离度量学习近年来引起了人们的广泛关注，其目标是基于用户反馈来学习距离度量。传统的度量学习方法主要集中在学习数据属性上的马氏距离度量。最近关于度量学习的研究已经扩展到序列数据，其中我们只有序列中的结构信息，但没有属性可用。然而，现实世界的应用通常涉及属性序列数据（例如，点击流），其中每个实例不仅包括一组属性（例如，用户会话上下文）而且是分类项的序列（例如，用户动作）。本文研究属性序列的度量学习问题。与以前的度量学习工作不同，我们现在需要在属性特征空间中超越Mahalanobis距离度量，同时还将结构信息纳入序列中。我们提出了一个深度学习框架，称为MLAS（Metric Learning on Attributed Sequences），用于学习有效测量属性序列之间差异的距离度量。真实世界数据集上的实证结果表明，所提出的MLAS框架显着提高性能的度量学习相比，国家的最先进的方法属性序列。
英文摘要：
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learning mainly focus on learning the Mahalanobis distance metric on data attributes. Recent research on metric learning has been extended to sequential data, where we only have structural information in the sequences, but no attribute is available. However, real-world applications often involve attributed sequence data (e.g., clickstreams), where each instance consists of not only a set of attributes (e.g., user session context) but also a sequence of categorical items (e.g., user actions). In this paper, we study the problem of metric learning on attributed sequences. Unlike previous work on metric learning, we now need to go beyond the Mahalanobis distance metric in the attribute feature space while also incorporating the structural information in sequences. We propose a deep learning framework, called MLAS (Metric Learning on Attributed Sequences), to learn a distance metric that effectively measures dissimilarities between attributed sequences. Empirical results on real-world datasets demonstrate that the proposed MLAS framework significantly improves the performance of metric learning compared to state-of-the-art methods on attributed sequences.
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