Principal Component Analysis is a one of the best way to reduce feature dimensionality. In this project, I developed PCA
and use in an example application. In pca.py
we take input data matrix as numpy array
like:
X = np.array([[-1, -1], [-1, 1], [1, -1], [1, 1]])
and apply PCA algorithm steps:
- Standardized Z matrix (
compute_Z(X)
) - We calculate covariance matrix, it is basically dot product of Z transpose and Z itself (
compute_covariance_matrix(Z)
) - Components, eigen values and eigen vectors (
find_pcs(COV)
) - Lastly, we project data (
project_data(Z, PCS, L, k, var)
)
This project developed for the Machine Learning Course (CS 691) at UNR