layout: default title: Principal Components Analysis
Principal Components Analysis
PCA is used to reduce high dimensional data set to lower dimensions. PCA can be used to identify patterns in data, express the data in a lower dimensional space. That way, similarities and differences can be highlighted. It is mostly used in face recognition and image compression. There are several flaws one has to be aware of when working with PCA:
- Linearity assumption - data is assumed to be linear combinations of some basis. There exist non-linear methods such as kernel PCA that alleviate that problem.
- Principal components are assumed to be orthogonal. ICA tries to cope with this limitation.
- Mean and covariance are assumed to be statistically important.
- Large variances are assumed to have important dynamics.
Parallelization strategy
Design of packages