Unsupervised classification of single-molecule data with autoencoders and transfer learning

Anton Vladyka, Tim Albrecht

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number035013
JournalMachine Learning: Science and Technology
Publication statusPublished - 21 Aug 2020


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