np mean(xaxis = [ , ,ut ]np linalg svd (( )/nprint (' \ ' )print ('\ diag( ^ ' [, sns kdeplot (zbw = [- - - - - - - - - - ]diag( ^ [ kernel density estimate the output above shows the principal component matrix (which we called uas well as the diagonal of matrix we see that large proportion of the variance /( + + %is explained by the first principal component thusit makes sense to transform each data point to > figure shows the kernel density estimate of the transformed data interestinglywe see two modesindicating at least two clusters in the data - - - - pca-combined data figure kernel density estimate of the pca-combined iris data further reading various information-theoretic measures to quantify uncertaintyincluding the shannon entropy and kullback-leibler divergencemay be found in [ the fisher informationthe prominent information measure in statisticsis discussed in detail in [ akaike' information criterion appeared in [ the em algorithm was introduced in [ and [ gives an in-depth treatment convergence proofs for the em algorithm may be found in [ classical reference on kernel density estimation is [ ]and [ is the main reference for the theta kernel density estimator theory and applications on finite mixture models may be found in [ for more details on clustering applications and algorithms as well as references on data compressionvector quantizationand pattern recognitionwe refer