Unsupervised classification of voltammetric data beyond principal component analysis

Christopher Weaver, Adrian C Fortuin, Anton Vladyka, Tim Albrecht*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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.

Original languageEnglish
Pages (from-to)10170-10173
Number of pages4
JournalChemical communications (Cambridge, England)
Volume58
Issue number73
Early online date17 Aug 2022
DOIs
Publication statusPublished - 13 Sept 2022

Keywords

  • Algorithms
  • Neural Networks, Computer
  • Principal Component Analysis

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