Linear methods of reducing dimensionality
		• PCA finds the directions that have the most variance. – By representing where each datapoint is along these axes, we minimize the squared reconstruction error. – Linear autoencoders are equivalent to PCA • Multi-Dimensional Scaling arranges the low-dimensional points so as to minimize the discrepancy between the pairwise distances in the original space and the pairwise distances in the low-D space. • MDS is equivalent to PCA (if we normalize the data right)