As shown at R-bloggers
autoencoder is better at reconstructing the original data set than PCA when k is small,
where k corresponds to the number of principal components in PCA or bottleneck dimension in AE,
however the error converges as k increases. For very large data sets this difference will be larger and means a smaller data set could be used for the same error as PCA. When dealing with big data this is an important property`.
The local adoption is ae_test.Rmd which produces ae_test.html and ae_test.pdf.
Additional work will be on variatinoal autoencoder (VAE) and denoising counterpart as indicated in the references below.
Bishop CM, Bishop H (2024), Deep learning: foundations and concepts, Springer International Publishing, DOI: 10.1007/978-3-031-45468-4.
Bludau I, Frank M, Dörig C. et al. Systematic detection of functional proteoform groups from bottom-up proteomic datasets. Nat Commun 12, 3810 (2021). https://doi.org/10.1038/s41467-021-24030-x.
Hofert M, Prasad A, Zhu M (2019). Quasi-Monte Carlo for multivariate distributions viagenerative neural networks. https://arxiv.org/abs/1811.00683, https://CRAN.R-project.org/package=gnn.
Kingma DP, Welling M (2014). Auto-Encoding Variational Bayes. https://arxiv.org/abs/1312.6114, https://keras.rstudio.com/articles/examples/variational_autoencoder.html.
Ng A. Sparse autoencoder, CS294A Lecture notes, https://web.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf.
Sattarov T, Herurkar D, Hees J (2023). Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data. Technical Report 2023-01. Deutsche Bundesban.
Trivadis SK (2017). Variational autoencoders for anomaly detection. https://rpubs.com/zkajdan/308801.
https://github.com/diazale/gt-dimred, https://github.com/lmcinnes/umap (https://umap-learn.readthedocs.io/en/latest/) and https://keras.io/examples/timeseries/timeseries_anomaly_detection/, https://www.mathworks.com/help/deeplearning/ug/anomaly-detection-using-autoencoder-and-wavelets.html, among others.