AI, Dimensionality Reduction, Machine Learning, Python, Data Science.
Independence: Principal components are orthogonal (uncorrelated), meaning they capture independent information, simplifying the interpretation of the reduced features. 3. Noise Reduction: PCA can help reduce noise by focusing on the components that explain the most significant variance in the data.