The All-or-Nothing Phenomenon in Sparse Tensor PCA.
Jonathan Niles-WeedIlias ZadikPublished in: NeurIPS (2020)
Keyphrases
- principal component analysis
- sparse pca
- dimensionality reduction
- sparse regression
- tensor decomposition
- tensor analysis
- high dimensional
- tensor factorization
- random projections
- sparse principal component analysis
- high order
- principal components analysis
- negative matrix factorization
- canonical correlation analysis
- principal components
- higher order
- sparse representation
- robust principal component analysis
- face images
- feature extraction
- feature space
- auxiliary information
- principle component analysis
- tensor space
- dimension reduction
- face recognition
- discriminant analysis
- signal recovery
- feature selection and classification
- dimensionality reduction methods
- high dimensional data
- linear subspace
- sparse data
- lower dimensional
- computer vision
- data representation
- independent component analysis
- covariance matrix
- feature selection