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Unsupervised Learning: Difference between revisions
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* Also known as Wasserstein metric | * Also known as Wasserstein metric | ||
==Dimension Reduction== | |||
Goal: Reduce the dimension of a dataset.<br> | |||
If each example <math>x \in \mathbb{R}^n</math>, we want to reduce each example to be in <math>\mathbb{R}^r<math> where <math>r < n</math> | |||
===PCA=== | |||
Principal Component Analysis<br> | |||
Preprocessing: Subtract the sample mean from each example so that the new sample mean is 0.<br> | |||
Goal: Find a vector <math>v_1</math> such that the projection <math>v_1 \cdot x</math> has maximum variance.<br> | |||
These principal components are the eigenvectors of <math>X^TX</math>.<br> | |||