The Intuition Behind Using ‘Variance’ in PCA

Principal Component Analysis (PCA) leverages variance preservation to reduce dimensions while retaining essential information. By retaining more variance during dimensionality reduction, less information is lost. PCA transforms data to create uncorrelated features and drops features based on their variance, which can be influenced by outliers. Further mathematical details about PCA, including vector projections, Lagrange Multipliers, and optimization steps, are available. Discussions on other machine learning topics like graph neural networks, NLP systems, quantization, and federated learning are also provided.