Reducing the number of features prevents the "curse of dimensionality" and speeds up training times for complex algorithms like Random Forests or Neural Networks. Practical Implementation
When preparing data for a machine learning model, the "mnf encode" process is a vital . mnf encode
The second step performs a standard PCA on the noise-whitened data. This separates the noise from the signal, resulting in a set of components (eigenvectors) where the initial components contain the most signal and the later components contain mostly noise. Why "Encode" with MNF? Reducing the number of features prevents the "curse
By shifting the noise into higher-order components, you can discard those components entirely, effectively "cleaning" the dataset before further analysis. you can discard those components entirely
Before training, raw spectral data is transformed into MNF space. Selection: Only the first