This document discusses principal component analysis (PCA), a dimensionality reduction technique. It explains that PCA calculates the covariance matrix of the data, finds the eigenvectors and eigenvalues, and uses the top eigenvectors as principal components to represent the data in a lower dimensional space while preserving as much information as possible. The original data can then be recovered from the lower dimensional representation.

1. Principle Component
Analysis
Dimensionality Reduction Technique
Utkarsh Sharma
Asst. Prof CSE Dept.
JUET(M.P.) India

3. Representation of PCA

4. Another Representation(A Simpler one…)

5. Consider the data
Y- Mean(Y)X- Mean(X)

6. Calculate the C0 variance Matrix

7. Calculate the Eigen vectors

8. Choose Principle Component

9. Deriving the final data

10. Final data with two eigen vectors

11. Final data with one eigen vector

12. Recover the original data