Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform a dataset into a set of orthogonal components that capture the maximum variance in the data. This method helps simplify complex data while preserving important relationships, making it easier to visualize and analyze. PCA is particularly useful in the context of autoencoders, as it can be used to initialize the network or analyze the learned representations, and it plays a crucial role in interpretability and explainability by revealing patterns in high-dimensional data.
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