skpcp

SkPCP: a package for robust principal component analysis or principal component pursuit built on top of RobustPCA <https://dl.acm.org/doi/10.1145/1970392.1970395/>

• The basic idea is to decompose a data matrix D into a low-rank matrix L and a sparse matrix S, i.e., D = L + S.

• Principal component pursuit aims at recovering L and S by solving the convex optimization problem:

  min $||L||_* + λ||S||_1$ subject to $D = L + S$, where the parameter $lambda > 0$ is a regularization parameter that balances the trade-off between the rank and sparsity.

Installation

pip install skpcp

Examples

• Example 1: Recovery of low-rank image corrupted by sparse noise
• Example 2: Recovery of low-rank image corrupted by sparse noise
• Example 3: Recovery of time series data corrupted by random spikes

API

• PCP