The autoencoder is trained using the fully efficient input of the dataset, which represents the healthy form of the training data. Eventually, the autoencoding model should be able to reconstruct the healthy version of the input data at any given point of time. Comparing the reconstructed ...
[23] employed linear allocation and convolutional autoencoders to construct a clustering model for spatiotemporal analysis, enabling the identification of patterns and trends within datasets to investigate the distri- bution and impacts of air pollutants. Various neural network architectures have also ...