自动特征-主题配对:空间表征学习中的语义对齐和嵌入空间

Dongjie Wang, Kunpeng Liu, David A. Mohaisen, Pengyang Wang, Chang-Tien Lu, Yanjie Fu
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引用次数: 5

摘要

空间数据的自动表征是一种重要的地理智能。空间表征学习(SRL)作为一种新兴的表征技术,利用深度神经网络(dnn)学习空间数据的非线性嵌入特征进行表征。然而,SRL通过dnn的内层提取特征,因此缺乏语义标签。另一方面,空间实体文本提供了对潜在特征标签的语义理解,但对深度SRL模型不敏感。我们如何教SRL模型在文本中发现适当的主题标签,并将学习到的特征与标签配对?提出了一个新的特征-主题配对问题,提出了一种新的基于粒子群算法的深度学习框架。具体来说,我们将特征-主题配对问题表述为1)潜在嵌入特征空间和2)文本语义主题空间之间的自动对齐任务。我们将两个空间的对齐分解为:1)点对齐,表示主题分布与嵌入向量之间的相关性;2)成对对齐,表示特征-特征相似矩阵与主题-主题相似矩阵之间的一致性。我们设计了一个基于粒子群算法的求解器来同时选择最优的主题集,并根据所选择的主题学习相应的特征。我们开发了一个闭环算法,在1)最小化表征重建和特征主题对齐的损失和2)搜索最佳主题之间迭代。最后,我们进行了大量的实验来证明我们的方法的增强性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.
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