A novel PCA-based approach for building on-board sensor classifiers for water contaminant detection.
Claudio De StefanoLuigi FerrignoFrancesco FontanellaLuca GereviniAlessandra Scotto di FrecaPublished in: Pattern Recognit. Lett. (2020)
Keyphrases
- principal component analysis
- lidar data
- automatic detection
- independent component analysis
- object detection
- training data
- support vector
- principal components analysis
- svm classifier
- false alarms
- machine learning algorithms
- detection method
- detection algorithm
- feature selection
- sensor data
- face recognition
- discriminative classifiers
- high sensitivity
- covariance matrix
- training samples
- naive bayes
- decision trees
- linear classifiers
- sensor networks
- principal components
- classification accuracy
- multiple classifiers
- multi sensor
- boosted classifiers
- principle component analysis
- water resources
- real time
- detection rate
- classification method
- test set
- training examples
- feature set
- dimensionality reduction
- face images
- multi class
- training set
- feature space
- machine learning
- neural network