Dimensionality Reduction by Low-Rank Embedding.
Chun-Guang LiXianbiao QiJun GuoPublished in: IScIDE (2012)
Keyphrases
- low rank
- dimensionality reduction
- high dimensional data
- nonlinear dimensionality reduction
- singular value decomposition
- low dimensional
- multidimensional scaling
- high dimensional
- linear combination
- low rank matrix
- kernel matrix
- high dimensionality
- convex optimization
- missing data
- manifold learning
- rank minimization
- data representation
- matrix decomposition
- pattern recognition
- matrix completion
- low rank matrices
- feature space
- vector space
- latent space
- subspace clustering
- subspace learning
- dimensionality reduction methods
- principal component analysis
- data points
- singular values
- kernel learning
- input space
- trace norm
- high order
- matrix factorization
- random projections
- semi supervised
- feature extraction
- data matrix
- sparse representation
- higher order
- minimization problems
- data sets
- robust principal component analysis
- kernel pca
- principal components
- original data
- euclidean distance
- feature selection
- computer vision
- machine learning
- neural network
- binary codes