Figure 4. PLSA approximates mixture coefficient better than PCA.

PCA and PLSA were performed on a simulated counts matrix with and different number of per-sample counts. The plot shows the average squared correlation coefficient between the true vectors and the three strongest principal components (in the case of PCA) or PLSA estimates . For each per-sample counts value 20 experiments were performed, and the plot gives the mean result and the standard error of the mean. The estimates obtained by PLSA show higher correlation with the true mixture proportions.