Randomized Matrix Sketching Algorithms
Published:
This project implements randomized numerical linear algebra algorithms for scalable machine learning and large-scale data analysis.
The goal is to reduce computational cost while preserving important matrix properties.
Algorithms implemented include:
- Leverage score sampling
- CountSketch
- Subspace embeddings
- Hutch++ trace estimation
Experiments evaluate the accuracy–efficiency trade-offs of sketching algorithms for:
- linear regression
- low-rank approximation
- matrix trace estimation
The implementations are written in Python and tested on high-dimensional synthetic and real datasets.