STPCA: Sparse tensor Principal Component Analysis for feature extraction.
Sujing WangMingfang SunYu-Hsin ChenEr-Ping PangChunguang ZhouPublished in: ICPR (2012)
Keyphrases
- principal component analysis
- feature extraction
- dimensionality reduction
- tensor analysis
- random projections
- tensor factorization
- tensor decomposition
- negative matrix factorization
- dimension reduction
- sparse representation
- linear discriminant analysis
- principal components
- high dimensional
- face recognition
- discriminant analysis
- sparse pca
- independent component analysis
- high order
- feature space
- covariance matrix
- low dimensional
- feature selection
- face images
- sparse data
- neural classifier
- kernel principal component analysis
- high dimensional data
- image classification
- singular value decomposition
- preprocessing
- dimensionality reduction methods
- feature representation
- feature vectors
- subspace learning
- image processing
- higher order
- support vector machine svm
- dictionary learning
- kernel pca
- sparse coding
- pattern recognition
- fisher linear discriminant
- wavelet transform
- matrix factorization
- manifold learning
- diffusion tensor
- extracting features
- pattern classification
- texture analysis
- frequency domain