Laplacian-Weighted Random Forest for High-Dimensional Data Classification.
Jianheng LiangDong HuangPublished in: SSCI (2019)
Keyphrases
- high dimensional data
- random forest
- high dimensionality
- decision trees
- feature set
- dimension reduction
- dimensionality reduction
- nearest neighbor
- low dimensional
- high dimensional
- data sets
- subspace clustering
- random forests
- data points
- machine learning
- decision tree learning algorithms
- data analysis
- feature space
- data distribution
- clustering high dimensional data
- feature selection
- ensemble classifier
- classification algorithm
- manifold learning
- lower dimensional
- multivariate temporal data
- support vector machine
- pattern recognition
- sparse coding
- linear discriminant analysis
- base classifiers
- image classification
- classification accuracy
- neural network
- data mining
- feature extraction
- support vector
- feature vectors
- multi class
- supervised learning
- k nearest neighbor
- knn
- ensemble methods
- benchmark datasets
- training data
- unsupervised learning
- face recognition
- subspace learning
- image segmentation
- training samples
- support vector machine svm