Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of kernel PCA in terms of hidden and visible units similar to Restricted Boltzmann Machines. This connection has led to insights on how to use kernel PCA in a generative procedure, called generative kernel PCA. In this paper, the use of generative kernel PCA for exploring latent spaces of datasets is investigated. New points can be generated by gradually moving in the latent space, which allows for an interpretation of the components. Firstly, examples of this feature space exploration on three datasets are shown with one of them leading to an interpretable representation of ECG signals. Afterwards, the use of the tool in combination with novelty detection is shown, where the latent space around novel patterns in the data is explored. This helps in the interpretation of why certain points are considered as novel.
翻译:内核五氯苯甲醚是一种强大的地物提取器,最近在受限制的内核机器(RKMs)方面出现了重新改造,这些中核聚物允许内核五氯苯甲醚以类似于受限制的波尔兹曼机器的隐藏和可见的单元表示,这种联系导致人们深入了解如何在基因化程序中使用内核五氯苯甲醚,称为基因内核五氯苯甲醚。本文调查了利用基因内核五氯苯甲醚探索潜在数据集空间的情况。通过在潜伏空间的逐步移动,可以产生新的点,从而可以解释这些组成部分。首先,在三个数据集上进行这种地心空间探索的例子与其中之一一起,可以解释ECG信号。随后,在探索数据中新模式的潜在空间时,展示了该工具与新发现相结合的用途。这有助于解释某些点为何被视为新颖。