Variational Autoencoder-Based Dimensionality Reduction for High-Dimensional Small-Sample Data Classification.
Mohammad Sultan MahmudJoshua Zhexue HuangXianghua FuPublished in: Int. J. Comput. Intell. Appl. (2020)
Keyphrases
- small sample
- dimensionality reduction
- high dimensional data
- high dimensional
- data points
- high dimensionality
- data analysis
- pattern recognition
- feature space
- data sets
- training data
- classification accuracy
- dimension reduction
- feature selection
- machine learning
- active learning
- support vector
- supervised learning
- similarity measure
- image classification
- low dimensional
- model selection
- decision trees
- image processing
- variable selection
- feature selection algorithms