Unsupervised classification of voltammetric data beyond principal component analysis
Autor: | Christopher Weaver, Adrian C. Fortuin, Anton Vladyka, Tim Albrecht |
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Rok vydání: | 2022 |
Předmět: | |
Zdroj: | Chemical Communications. 58:10170-10173 |
ISSN: | 1364-548X 1359-7345 |
Popis: | In this study, we evaluate different apoproaches to unsupervised classification of cyclic voltammetric data, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) as well as neural networks. To this end, we exploit a form of transfer learning, based on feature extraction in an image recognition network, VGG-16, in combination with PCA, t-SNE or UMAP. Overall, we find that t-SNE performs best when applied directly to numerical data (noise-free case) or to features (in the presence of noise), followed by UMAP and then PCA. |
Databáze: | OpenAIRE |
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