Principal Component

Principal Component Analysis (PCA) is a dimensionality reduction technique that identifies principal components—linear combinations of original variables—capturing the maximum variance in a dataset. Current research focuses on extending PCA's capabilities, including nonlinear variants like Kernel PCA (KPCA) for improved feature extraction and out-of-distribution detection, and integrating PCA with other methods such as deep learning architectures (e.g., autoencoders, neural networks) for enhanced performance in various applications. This versatile technique finds widespread use in diverse fields, from accelerating computationally expensive simulations and improving the interpretability of complex models (like CNNs) to enhancing data analysis in genomics, hyperspectral imaging, and music recommendation systems.