高维数据的交互式可视化分析

Haesun Park
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引用次数: 0

摘要

许多现代数据集可以在高维向量空间中表示,并受益于利用数值线性代数和优化等先进技术的计算方法。由于利用了自动化算法和人类快速的视觉感知和交互,视觉分析方法对数据理解和分析做出了巨大贡献。然而,由于低维屏幕空间和有限的像素来表示数据,针对高维大规模数据的可视化分析一直具有挑战性。在支持视觉分析的各种计算技术中,降维和聚类通过将维度和体积降低到视觉可管理的规模而发挥了重要作用。在这次演讲中,我们介绍了一些监督降维的关键基础方法,如线性判别分析(LDA),通过非负矩阵分解(NMF)进行降维和聚类/主题发现,以及通过正交Procrustes进行有效融合和比较的视觉空间排列。我们演示了这些方法如何有效地支持涉及大规模文档和图像数据集的交互式可视化分析任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactive visual analytics for high dimensional data
Many modern data sets can be represented in high dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from numerical linear algebra and optimization. Visual analytics approaches have contributed greatly to data understanding and analysis due to utilization of both automated algorithms and human's quick visual perception and interaction. However, visual analytics targeting high dimensional large-scale data has been challenging due to low dimensional screen space with limited pixels to represent data. Among various computational techniques supporting visual analytics, dimension reduction and clustering have played essential roles by reducing the dimension and volume to visually manageable scales. In this talk, we present some of the key foundational methods for supervised dimension reduction such as linear discriminant analysis (LDA), dimension reduction and clustering/topic discovery by nonnegative matrix factorization (NMF), and visual spatial alignment for effective fusion and comparisons by Orthogonal Procrustes. We demonstrate how these methods can effectively support interactive visual analytic tasks that involve large-scale document and image data sets.
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