Feature Manipulation VIs
- Updated2023-02-21
- 1 minute(s) read
Owning Palette: Analytics and Machine Learning VIs
Requires: Analytics and Machine Learning Toolkit. This topic might not match its corresponding palette in LabVIEW depending on your operating system, licensed product(s), and target.
Use the Feature Manipulation VIs to train and deploy feature reduction and normalization models.
Feature reduction reduces the dimension of data so that you can apply machine learning algorithms to the training data. Normalization standardizes the range of features for the training data.
| Palette Object | Description |
|---|---|
| Deploy Feature Manipulation Model | Deploys a trained feature manipulation model on deployment data. |
| Fisher | Trains a Fisher's linear discriminant model. You can use the Fisher's linear discriminant model to reduce the dimension of training data. As a supervised model, Fisher's linear discriminant requires both healthy data and abnormal data. |
| KPCA | Trains a kernel principal component analysis (KPCA) model. You can use the KPCA model to reduce the dimension of training data. |
| Normalize | Trains a normalization model. You can use the normalization model to normalize training data with Z-Score or Min-Max method. |
| PCA | Trains a principal component analysis (PCA) model. You can use the PCA model to reduce the dimension of training data. |
| Set Feature Manipulation Model | Sets properties for a trained feature manipulation model before deployment. |