空间编码和多模态分析的表示解释

Ninghao Liu, Mengnan Du, Xia Hu
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引用次数: 14

摘要

表示学习模型将数据实例映射到低维向量空间,从而促进后续模型(如分类和聚类模型)的部署,或下游应用程序(如推荐和异常检测)的实现。然而,表征学习的结果很难被用户直接理解,因为潜在空间的每个维度可能没有任何特定的含义。理解表征学习对许多应用都是有益的。例如,在推荐系统中,知道为什么用户实例被映射到潜在空间中的某个位置可能会揭示用户的兴趣和概况。在本文中,我们提出了一个解释框架来理解和描述表示向量在潜在空间中的分布。具体来说,我们设计了一种编码方案,将表示实例转换为空间代码,以表明它们在潜在空间中的位置。然后,构建了一个多模态自编码器,用于生成给定其空间代码的表示实例的描述。该编码方案支持以不同粒度指示位置。自编码器的加入使得该框架能够处理不同类型的数据。设计了几个指标来评估解释结果。在不同的应用场景和不同的表示学习模型下进行了实验,验证了该框架的灵活性和有效性。
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Representation Interpretation with Spatial Encoding and Multimodal Analytics
Representation learning models map data instances into a low-dimensional vector space, thus facilitating the deployment of subsequent models such as classification and clustering models, or the implementation of downstream applications such as recommendation and anomaly detection. However, the outcome of representation learning is difficult to be directly understood by users, since each dimension of the latent space may not have any specific meaning. Understanding representation learning could be beneficial to many applications. For example, in recommender systems, knowing why a user instance is mapped to a certain position in the latent space may unveil the user's interests and profile. In this paper, we propose an interpretation framework to understand and describe how representation vectors distribute in the latent space. Specifically, we design a coding scheme to transform representation instances into spatial codes to indicate their locations in the latent space. Following that, a multimodal autoencoder is built for generating the description of a representation instance given its spatial codes. The coding scheme enables indication of position with different granularity. The incorporation of autoencoder makes the framework capable of dealing with different types of data. Several metrics are designed to evaluate interpretation results. Experiments under various application scenarios and different representation learning models are conducted to demonstrate the flexibility and effectiveness of the proposed framework.
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