Kernel alignment unsupervised discriminative dimensionality reduction.
Yunlong GaoSi-Zhe LuoJinyan PanZhihao WangPeng GaoPublished in: Neurocomputing (2021)
Keyphrases
- dimensionality reduction
- unsupervised learning
- class separability
- discriminant projection
- kernel learning
- semi supervised
- feature space
- kernel pca
- input space
- kernel discriminant analysis
- kernel trick
- feature extraction
- high dimensional
- class discrimination
- principal component analysis
- feature selection
- low dimensional
- graph embedding
- high dimensional data
- latent space
- unsupervised feature selection
- linear discriminant analysis
- manifold learning
- data representation
- kernel function
- high dimensionality
- dimensionality reduction methods
- manifold alignment
- data points
- pattern recognition
- lower dimensional
- kernel methods
- profile hidden markov models
- computer vision
- subspace learning
- supervised learning
- semi supervised learning
- dimension reduction
- discriminant analysis
- pairwise constraints
- labeled data
- pattern recognition and machine learning
- euclidean distance
- principal components
- optimal kernel
- weakly supervised
- linear dimensionality reduction
- structure preserving
- nonlinear dimensionality reduction
- metric learning
- sparse coding
- kernel machines
- fisher kernel
- euclidean space
- kernel matrix
- multiple kernel learning
- support vector