Principal Component Analysis

Principal Component Analysis (PCA) is a widely used dimensionality reduction technique aiming to identify the principal components—the directions of greatest variance in a dataset—to simplify data representation while preserving essential information. Current research focuses on improving PCA's robustness to outliers and noise, developing distributed and efficient algorithms for large datasets (including adaptations for quantum computing and federated learning), and integrating PCA with other machine learning methods for tasks like classification, regression, and anomaly detection. PCA's impact spans diverse fields, enhancing data analysis in applications ranging from image processing and sensor data compression to biomedical signal analysis and industrial process monitoring.