论文标题
非线性维度降低的特征学习，以最大程度地提取隐藏模式
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
论文作者
论文摘要
降低（DR）在高维数据的视觉分析中起着至关重要的作用。 DR的一个主要目的是揭示存在于内在的低维歧管上的隐藏模式。但是，当歧管被某些有影响力的数据属性扭曲或掩盖时，DR通常会忽略重要模式。本文介绍了一个功能学习框架FealM，旨在为非线性DR生成一组优化的数据投影，以便在隐藏的歧管中捕获重要模式。这些预测产生了最大不同的最近邻居图，因此由此产生的DR结果显着差异。为了获得这种功能，我们设计了一种优化算法，并引入了一个新的图形差异度量，称为邻居形状差异。此外，我们开发了交互式可视化，以帮助比较获得的DR结果和每个DR结果的解释。我们通过使用合成和现实世界数据集通过实验和案例研究来证明菲尔姆的有效性。
Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are distorted or masked by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate a set of optimized data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, named neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM's effectiveness through experiments and case studies using synthetic and real-world datasets.
