Self-supervised backprop and PCA
	• If the hidden and output layers are linear, it will learn hidden units that are a linear function of the data and minimize the squared reconstruction error. • The m hidden units will span the same space as the first m principal components – Their weight vectors may not be orthogonal – They will tend to have equal variances