A generalized least-squares approach regularized with graph embedding for dimensionality reduction.
Xiang-Jun ShenSi-Xing LiuBing-Kun BaoChunhong PanZheng-Jun ZhaJianping FanPublished in: Pattern Recognit. (2020)
Keyphrases
- graph embedding
- least squares
- dimensionality reduction
- low dimensional
- supervised dimensionality reduction
- discriminant analysis
- data representation
- semi supervised dimensionality reduction
- high dimensional
- semi supervised
- pattern recognition
- unsupervised learning
- subspace learning
- embedding space
- feature selection
- principal component analysis
- feature extraction
- feature representation
- high dimensional data
- feature space
- manifold learning
- data points
- optical flow
- discriminant embedding
- data sets
- dimensionality reduction methods
- face recognition
- euclidean distance
- dictionary learning
- nonlinear dimensionality reduction
- similarity measure
- discriminant information
- linear discriminant analysis
- geometric properties
- computer vision