论文标题
Voxel2VEC：一种自然语言处理方法，用于学习科学数据的分布式表示形式
voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data
论文作者
论文摘要
科学数据中的关系，例如单变量数据中特征的数值和空间分布关系，多元数据中标量值组合的关系以及时间变化和整体数据中的体积的关联，是复杂的且复杂的。本文介绍了一种新型的无监督表示学习模型Voxel2Vec，该模型用于学习低维矢量空间中标量值/标量值组合的分布式表示。它的基本假设是，如果两个标量值/标量值组合具有相似的上下文，则它们通常在特征方面具有很高的相似性。通过将标量值/标量值组合表示为符号，voxel2vec在空间分布的背景下了解了它们之间的相似性，然后允许我们通过传输预测来探索卷之间的整体关联。我们通过将其与单变量数据的等速度相似性图进行比较，并将学习的分布式表示形式与多变量数据分类以及用于时间变化和集合数据的关联分析来证明voxel2vec的有用性和有效性。
Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.
