Unsupervised classification of voltammetric data beyond principal component analysis

Autor:	Christopher Weaver, Adrian C. Fortuin, Anton Vladyka, Tim Albrecht
Rok vydání:	2022
Předmět:	Principal Component Analysis Materials Chemistry Metals and Alloys Ceramics and Composites Neural Networks Computer General Chemistry Algorithms Catalysis Surfaces Coatings and Films Electronic Optical and Magnetic Materials
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
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::217d4a8577a24dedbdfc65e8b1c05de4 https://doi.org/10.1039/d2cc03187f Zobrazit plný text záznamu