论文标题
分位数量式嵌入分布转换和歧管嵌入，并具有选择嵌入分布的能力
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution
论文作者
论文摘要
我们提出了一种新的嵌入方法，称为分位数定量嵌入（QQE），用于分布转换和歧管嵌入，并具有选择嵌入分布的能力。 QQE使用来自视觉统计测试的分位数量化图的概念，可以将数据的分布转换为任何理论所需的分布或经验参考样本。此外，QQE为用户提供了嵌入分布的选择，以嵌入数据的歧管中的低维嵌入空间。它也可以用于修改其他降低方法的嵌入分布，例如PCA，T-SNE和深度度量学习，以更好地表示或可视化数据。我们提出了无监督和监督形式的QQE。 QQE还可以将分布转换为精确的参考分布或其形状。我们表明，QQE可以更好地歧视类，在某些情况下。我们对不同合成和图像数据集的实验显示了所提出的嵌入方法的有效性。
We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile-quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding the manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of other dimensionality reduction methods, such as PCA, t-SNE, and deep metric learning, for better representation or visualization of data. We propose QQE in both unsupervised and supervised forms. QQE can also transform a distribution to either an exact reference distribution or its shape. We show that QQE allows for better discrimination of classes in some cases. Our experiments on different synthetic and image datasets show the effectiveness of the proposed embedding method.
