A deep manifold learning approach for spatial-spectral classification with limited labeled training samples.
Xichuan ZhouNian LiuFang TangYingjun ZhaoKai QinLei ZhangDong LiPublished in: Neurocomputing (2019)
Keyphrases
- training samples
- manifold learning
- feature space
- high dimensional
- training set
- supervised learning
- training data
- low dimensional
- decision boundary
- dimensionality reduction
- nearest neighbor classifier
- semi supervised
- feature extraction
- dimension reduction
- learning algorithm
- unlabeled samples
- test sample
- discriminative information
- high dimensional data
- subspace learning
- decision trees
- face images
- base classifiers
- feature vectors
- data sets
- support vector machine
- unsupervised learning
- machine learning
- pattern classification
- class labels
- data mining
- feature selection
- support vectors
- pattern recognition
- sparse representation
- linear combination
- semi supervised learning
- labeled data
- image classification
- multi class