Low-Dimensional Tensor Principle Component Analysis.
Hayato ItohAtsushi ImiyaTomoya SakaiPublished in: CAIP (1) (2015)
Keyphrases
- principle component analysis
- low dimensional
- dimensionality reduction
- lower dimensional
- dimension reduction
- high dimensional
- principal component analysis
- random projections
- feature space
- high dimensional data
- high dimensional feature space
- manifold learning
- dimensionality reduction methods
- feature extraction
- input space
- euclidean space
- multidimensional scaling
- principal components
- data points
- higher dimensional
- pattern recognition
- kernel pca
- subspace learning
- vector space
- linear discriminant analysis
- unsupervised learning
- original data
- singular value decomposition
- face recognition
- svm classifier
- riemannian manifolds
- principal components analysis
- covariance matrix
- high dimensionality
- data sets
- feature selection
- machine learning
- euclidean distance
- sparse representation
- input data
- computer vision