Dimensionality Reduction: Non-linear methods

In the quest to understand and interpret high-dimensional data, dimensionality reduction plays a pivotal role. High-dimensional data presents a unique set of challenges; each additional dimension can exponentially increase the complexity, making visualization and analysis daunting. As we saw in our previous article[2], traditional linear methods, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), project data into lower dimensions while preserving as much variance or information as possible. However, these methods often fall short when dealing with the intricate, non-linear structures inherent in many real-world datasets.

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