Isomap Embedding -- An Awesome Approach to Non-linear Dimensionality Reduction

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As you can see, Isomap is an Unsupervised Machine Learning technique aimed at Dimensionality Reduction. It differs from a few other techniques in the same category by using a non-linear approach to dimensionality reduction instead of linear mappings used by algorithms such as PCA. We will see how linear vs. non-linear approaches differ in the next section. Isomap is a technique that combines several different algorithms, enabling it to use a non-linear way to reduce dimensions while preserving local structures. Before we look at the example of Isomap and compare it to a linear method of Principal Components Analysis (PCA), let's list the high-level steps that Isomap performs: For our example, let's create a 3D object known as a Swiss roll.

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