Anomaly Detection VIs

LabVIEW 2018 Analytics and Machine Learning Toolkit Help

Edition Date: July 2018

Part Number: 377059B-01

»View Product Info
Download Help (Windows Only)

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 Anomaly Detection VIs to initialize, train, and deploy unsupervised anomaly detection models that detect anomalies in unlabeled data.

Palette ObjectDescription
Deploy Anomaly Detection ModelDeploys a trained anomaly detection model and returns the health index of input data.
Initialize Anomaly Detection Model (GMM-CV)Initializes the hyperparameters of the Gaussian mixture model (GMM) algorithm.
Initialize Anomaly Detection Model (One-Class SVM)Initializes the hyperparameters of the one-class support vector machine (SVM) algorithm. This VI uses the nu-SVM algorithm.
Initialize Anomaly Detection Model (PCA T2Q)Initializes the hyperparameters of the principal component analysis (PCA) algorithm.
Initialize Anomaly Detection Model (SOM-MQE)Initializes the hyperparameters of the self-organizing map (SOM) algorithm.
Set Anomaly Detection ModelSets properties for a trained anomaly detection model before deployment.
Train Anomaly Detection ModelTrains an anomaly detection model.

WAS THIS ARTICLE HELPFUL?

Not Helpful