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.
翻译:我们提出一种新的嵌入方法,名为“量子-量子嵌入”(QQE),用于分配变换和嵌入能力选择嵌入分布分布的多重嵌入。“E”使用视觉统计测试中的量化-量子图块的概念,可以将数据分布转换为任何理论所需的分布或经验参考样本。此外,“E”让用户选择嵌入分布方法,将数据元件嵌入低维嵌入空间。它也可以用于修改其他维度减少方法的嵌入分布,如五氯苯甲醚、T-SNE和深度计量学习,以便更好地表述或直观数据。我们提议采用不受监督和监督的形式。“E”也可以将分布转换为精确的参考分布或其形状。我们显示“E”允许在某些情况下更好地区分各个类别。我们在不同的合成和图像数据集上进行的实验显示了拟议嵌入方法的有效性。