How to Reduce Dimension With PCA and Random Projections?
Fan YangSifan LiuEdgar DobribanDavid P. WoodruffPublished in: IEEE Trans. Inf. Theory (2021)
Keyphrases
- model selection
- random projections
- principal component analysis
- dimensionality reduction
- dimension reduction
- compressive sensing
- compressed sensing
- original data
- lower dimensional
- sparse representation
- low dimensional
- random sampling
- image reconstruction
- hash functions
- data sets
- feature space
- high dimensionality
- document clustering
- digital libraries
- face recognition
- unsupervised learning
- information retrieval systems
- natural language processing
- upper bound
- pattern recognition
- information retrieval