AI, Dimensionality Reduction, Machine Learning, Python, Data Science.
points then X : m*n P : k*m Y = PX : (k*m)(m*n) = (k*n) Hence, our new transformed matrix has n data points having k dimensions. Pros: 1. Dimensionality Reduction: PCA effectively reduces the number of features, which is beneficial for models that suffer from the curse of dimensionality. 2. Feature