Datasets from single-molecule experiments often reflect a large variety of molecular behaviour. The exploration of such datasets can be challenging, especially if knowledge about the data is limited and a priori assumptions about expected data characteristics are to be avoided. Indeed, searching for pre-defined signal characteristics is sometimes useful, but it can also lead to information loss and the introduction of expectation bias. Here, we demonstrate how Transfer Learning-enhanced dimensionality reduction can be employed to identify and quantify hidden features in single-molecule charge transport data, in an unsupervised manner. Taking advantage of open-access neural networks trained on millions of seemingly unrelated image data, our results also show how Deep Learning methodologies can readily be employed, even if the amount of problem-specific, ‘own’ data is limited.

中文翻译：

---

使用自动编码器和转移学习对单分子数据进行无监督分类

来自单分子实验的数据集通常反映出各种各样的分子行为。对此类数据集的探索可能具有挑战性，尤其是在有关数据的知识有限且要避免有关预期数据特征的先验假设的情况下。确实，搜索预定义的信号特征有时是有用的，但它也可能导致信息丢失和期望偏差的引入。在这里，我们演示了如何以无监督的方式利用转移学习增强的降维方法来识别和量化单分子电荷传输数据中的隐藏特征。利用对数百万个看似无关的图像数据进行训练的开放式访问神经网络的优势，我们的结果还表明，可以轻松地采用深度学习方法，