Practical ReProCS for Separating Sparse and Low-dimensional Signal Sequences from their Sum.
Han GuoChenlu QiuNamrata VaswaniPublished in: CoRR (2013)
Keyphrases
- low dimensional
- high dimensional
- compressive sensing
- random projections
- signal processing
- high dimensional data
- signal recovery
- dimensionality reduction
- input space
- data points
- sparse data
- real world
- eigenvalue decomposition
- feature space
- hidden markov models
- manifold learning
- high frequency
- compressed sensing
- denoising
- principal component analysis
- dimension reduction
- sequential patterns
- euclidean space
- data sets
- non stationary
- image representation
- nonlinear dimensionality reduction