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Data sepapability Test -- to build an accurate model for the process of a system under study, the first step is to separate data of different clusters (categories). The figures below compare PCA and MasterMiner method.
Fig-1 PCA - No separation of data. |
Principal Component Analysis
(PCA) or Kohenum-Louve Transform: Projection in maximum separable direction. Good for linear, Gaussian cases without noise. All data are used in building a model. Fisher's Method: Line projection with maximum distance between clusters. Result is similar to that of PCA. An example is in Fig-1. |
Fig-2 Good separation by hidden projection of MasterMiner™ |
MasterMiner™: Based on a projective geometry (hidden projection) method. It is well suited for nonlinear (or linear), non-Gaussian (or Gaussian) cases with noise. Only a sub set of data are used in building a model for the underlying process. It's data separability is superior to that of either PCA or Fisher. For the same data, MasterMiner gives much better data separation. For comparison with PCA, see result in Fig-2. |
