Reducing the dimensionality of data has many valuable potential uses. The low-dimensional version of the data can be used for visualisation, or for further processing in a modelling pipeline. The low-dimensional version should capture only the salient features of the data, and can indeed be seen as a form of compression. Many techniques for dimensionality reduction exists, including PCA (and its kernelized variant Kernel PCA), Locally Linear Embedding, ISOMAP, UMAP, Linear Discriminant Analysis, and t-SNE. Some of these are linear methods, while others are non-linear methods. Many of the non-linear methods falls into a class of algorithms known as manifold learning algorithms.
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