Self-supervised backprop in a linear network
		• 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. – This is exactly what Principal Components Analysis does. • The M hidden units will span the same space as the first M principal components found by PCA – Their weight vectors may not be orthogonal – They will tend to have equal variances