Principal component analysis (PCA) is a linear dimensionality reduction technique.
Advantages of PCA / SVD
- State of the art for many applications (supervised and unsupervised)
- Incredibly efficient (often, almost linear time)
- Strong theoretical background
- Can also be used to store data in more efficient way (image compression)
- Visual evaluation possible for small number of components (2 or 3)