Principle Component Analysis
It is a dimension reduction technique.
It transforms the variables into a new set of variables as principal components.
These principal components are linear combinations of original variables and are orthogonal.
PCA Algorithm:
1. Get data
2. Compute mean vector
3. Subtract mean from the given data
4. calculate the covariance matrix.
5. calculate the eigen vectors and eigen values of the covariance matrix.
6. Choosing components and forming a feature vector.
7. Deriving the new data set.
Resources for practice
