voxel2vec：一种学习科学数据分布式表示的自然语言处理方法
voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data
摘要：
科学数据中的关系，如单变量数据中特征的数值和空间分布关系、多变量数据中标量值组合的关系、时变数据和集合数据中体积的关联等，是错综复杂的。本文提出了一种新的无监督表示学习模型voxel 2 vec，用于学习低维向量空间中标量值/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高相似性。通过将标量值/标量值组合表示为符号，voxel 2 vec在空间分布的上下文中学习它们之间的相似性，然后允许我们通过转移预测来探索体积之间的整体关联。我们通过将其与单变量数据的等值面相似性图进行比较，并将学习到的分布式表示应用于多变量数据的特征分类和时变和集成数据的关联分析，来证明voxel 2 vec的有用性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
