Hierarchical Stochastic Neighbor Embedding
Hierarchical Stochastic Neighbor Embedding
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DOI：
10.1111/cgf.12878
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发表时间：
2016-06-01
影响因子：
2.5
通讯作者：
Vilanova, A.
中科院分区：
文献类型：
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作者：
Pezzotti, N.;Hollt, T.;Vilanova, A.
近年来，降维技术得到了发展，并广泛应用于探索性数据分析中的假设生成。然而，这些技术面临着在计算时间和所提供降维质量之间进行权衡的问题。在这项工作中，我们通过引入分层随机近邻嵌入（Hierarchical - SNE）来解决这一局限性。利用数据的分层表示，我们将众所周知的“先总体，按需看细节”的理念融入非线性降维中。首先，分析展示了一种嵌入，它只揭示数据中的主要结构（总体）。然后，通过选择在总体中可见的结构，用户可以对数据进行筛选，并在分层结构中深入探究。当用户深入分层结构时，高维结构的详细可视化将带来新的见解。在本文中，我们解释了分层随机近邻嵌入如何扩展到对大数据集的分析。此外，我们展示了它在深度学习架构可视化和高光谱图像分析中的应用潜力。
In recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embedding (Hierarchical-SNE). Using a hierarchical representation of the data, we incorporate the well-known mantra of Overview-First, Details-On-Demand in non-linear dimensionality reduction. First, the analysis shows an embedding, that reveals only the dominant structures in the data (Overview). Then, by selecting structures that are visible in the overview, the user can filter the data and drill down in the hierarchy. While the user descends into the hierarchy, detailed visualizations of the high-dimensional structures will lead to new insights. In this paper, we explain how Hierarchical-SNE scales to the analysis of big datasets. In addition, we show its application potential in the visualization of Deep-Learning architectures and the analysis of hyperspectral images.