Figure 2
From: ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics
Summary of non-negative matrix factorization (NMF) applied to ILI diagnostic claims claims data. (A) Reconstruction error or the fraction of unexplained variance for PCA (red) and NMF (black) versus the subspace s selected. (B) Change in reconstruction error for PCA and NMF as compared to the change in reconstruction error for PCA performed on a scrambled version of the input matrix A. PCAscram shown in gray line is used to estimate the cut-off number of dimensions, beyond which the dimensionality reduction method explains only noise within the dataset. For our analysis, s beyond 12 is only explaining noise in the data, as is evident from the intersection between the gray and black/red lines.