Compressive PCA for Low-Rank Matrices on Graphs.
Nauman ShahidNathanaël PerraudinGilles PuyPierre VandergheynstPublished in: IEEE Trans. Signal Inf. Process. over Networks (2017)
Keyphrases
- low rank matrices
- principal component analysis
- singular value decomposition
- random projections
- low rank
- dimensionality reduction
- face recognition
- covariance matrix
- principal components
- directed graph
- feature extraction
- pattern recognition
- dimension reduction
- graph matching
- high dimensional
- graph partitioning
- feature space
- mutual information
- graph structure
- weighted graph
- graph representation
- negative matrix factorization
- low rank matrix