Manifold learning with structured subspace for multi-label feature selection.
Yuling FanJinghua LiuPeizhong LiuYongzhao DuWeiyao LanShunxiang WuPublished in: Pattern Recognit. (2021)
Keyphrases
- manifold learning
- multi label
- feature selection
- dimensionality reduction
- text categorization
- low dimensional
- subspace learning
- text classification
- feature space
- high dimensional data
- feature extraction
- high dimensional
- multi label classification
- dimension reduction
- high dimensionality
- image classification
- image annotation
- lower dimensional
- graph cuts
- binary classification
- principal component analysis
- linear subspace
- input space
- support vector
- machine learning
- learning tasks
- sparse representation
- linear discriminant analysis
- feature subspace
- k nearest neighbor
- feature set
- semi supervised
- data points
- knn
- class labels
- unsupervised learning
- classification accuracy
- training samples
- model selection
- multi class
- neural network
- max margin
- training set
- multi task
- higher order
- feature subset
- semi supervised learning
- unlabeled data