Dimensionality reduction methods, also known as projections, are often used to explore multidimensional data in machine learning, data science, and information visualization. However, several such methods, such as the well-known t-distributed stochastic neighbor embedding and its variants, are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct any such projections. We train a deep neural network based on sample set drawn from a given data universe, and their corresponding two-dimensional projections, compute with any user-chosen technique. Next, we use the network to infer projections of any dataset from the same universe. Our approach generates projections with similar characteristics as the learned ones, is computationally two to four orders of magnitude faster than existing projection methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high-dimensional datasets from machine learning.

中文翻译：

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深度学习多维投影

降维方法，也称为投影，通常用于探索机器学习、数据科学和信息可视化中的多维数据。然而，一些这样的方法，例如众所周知的 t 分布随机邻居嵌入及其变体，对于大型数据集来说计算成本很高，存在稳定性问题，并且不能直接处理样本外数据。我们提出了一种构建任何此类预测的学习方法。我们基于从给定数据域中抽取的样本集及其相应的二维投影来训练深度神经网络，并使用任何用户选择的技术进行计算。接下来，我们使用网络来推断来自同一宇宙的任何数据集的投影。我们的方法生成的预测与学习的预测具有相似的特征，在计算上比现有的投影方法快两到四个数量级，没有复杂的用户参数，以稳定的方式处理样本外数据，可用于学习任何投影技术。我们在来自机器学习的几个真实世界的高维数据集上展示了我们的建议。