A way of thinking about the role of the inverse covariance matrix
	• If the Gaussian is spherical we don’t need to worry about the covariance matrix. • So we could start by transforming the data space to make the Gaussian spherical – This is called “whitening” the data. – It pre-multiplies by the matrix square root of the inverse covariance matrix. • In the transformed space, the weight vector is just the difference between the transformed means.