تقرير
Unsupervised Classification of Single-Molecule Data with Autoencoders and Transfer Learning
| العنوان: | Unsupervised Classification of Single-Molecule Data with Autoencoders and Transfer Learning |
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| المؤلفون: | Vladyka, Anton, Albrecht, Tim |
| سنة النشر: | 2020 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Data Analysis, Statistics and Probability |
| الوصف: | 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. Comment: 23 pages in total, incl. supporting information; 8 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2004.01239 |
| رقم الأكسشن: | edsarx.2004.01239 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |