unsupervised learning and PCA

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118 Jonathan Richard Shewchuk 20 Unsupervised Learning: Principal Components Analysis UNSUPERVISED LEARNING We have sample points, but no labels! No classes, no y-values, nothing to predict. Goal: Discover structure in the data. Examples: – Clustering: partition data into groups of similar/nearby points. – Dimensionality reduction: data often lies near a low-dimensional subspace (or manifold) in feature space; matrices have low-rank approximations. [Whereas clustering is about grouping similar sample points, dimensionality reduction…

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