Fast computation of PCA bases of image subspace using its inner-product subspace.
E. S. Gopi SubbuPalanisamy PonnusamyPublished in: Appl. Math. Comput. (2013)
Keyphrases
- principal component analysis
- principal components
- dimensionality reduction
- low dimensional
- principal components analysis
- basis vectors
- image data
- input image
- linear subspace
- image analysis
- multiscale
- subspace methods
- lower dimensional
- feature space
- image features
- eigendecomposition
- image content
- image classification
- single image
- high dimensional
- feature extraction
- image regions
- subspace learning
- image set
- covariance matrix
- higher order singular value decomposition
- linear discriminant analysis
- high dimensional data
- image representation
- edge detection
- basis functions
- image processing
- discriminant information
- dimension reduction
- feature subspace
- image sequences
- high resolution
- kernel pca
- face recognition
- k means
- subspace analysis
- extracting features
- principle component analysis
- image retrieval
- face images
- random projections
- subspace clustering
- input data
- linear combination
- computer vision