论文标题
一种用于对比的主体组件分析的在线算法
An online algorithm for contrastive Principal Component Analysis
论文作者
论文摘要
在数据分析中，找到可以在大型数据集中有效计算的信息的低维表示是一个重要的问题。最近，提出了对比性主成分分析（CPCA），作为利用对比度学习的PCA的更有信息的概括。但是，CPCA的性能对高参数选择敏感，目前尚无用于实施CPCA的在线算法。在这里，我们介绍了一种修改的CPCA方法，我们表示CPCA*，该方法对高参数的选择更容易解释，并且不太敏感。我们得出了一种用于CPCA*的在线算法，并证明它将其映射到具有本地学习规则的神经网络上，因此它可以在节能神经形态硬件中实现。我们评估了在实际数据集上在线算法的性能，并突出显示与原始配方的差异和相似之处。
Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive learning. However, the performance of cPCA is sensitive to hyper-parameter choice and there is currently no online algorithm for implementing cPCA. Here, we introduce a modified cPCA method, which we denote cPCA*, that is more interpretable and less sensitive to the choice of hyper-parameter. We derive an online algorithm for cPCA* and show that it maps onto a neural network with local learning rules, so it can potentially be implemented in energy efficient neuromorphic hardware. We evaluate the performance of our online algorithm on real datasets and highlight the differences and similarities with the original formulation.
