The All-or-Nothing Phenomenon in Sparse Tensor PCA.
Jonathan Niles-WeedIlias ZadikPublished in: CoRR (2020)
Keyphrases
- principal component analysis
- dimensionality reduction
- sparse pca
- tensor decomposition
- high dimensional
- tensor analysis
- tensor factorization
- sparse regression
- high order
- negative matrix factorization
- principal components analysis
- sparse principal component analysis
- sparse representation
- random projections
- canonical correlation analysis
- face recognition
- sparse data
- principle component analysis
- feature extraction
- sparse coding
- feature space
- robust principal component analysis
- feature selection and classification
- covariance matrix
- principal components
- data representation
- appearance based object recognition
- kernel pca
- low dimensional
- sparse matrix
- face images
- higher order
- diffusion tensor
- compressive sensing
- low rank
- signal processing
- dimension reduction
- k means
- high resolution
- feature dimension
- auxiliary information
- dictionary learning
- image classification
- missing data
- singular value decomposition
- linear discriminant analysis