Using Singular Value Decomposition (SVD) for Dimensionality Reduction and Latent Features
Introduction Singular Value Decomposition (SVD) is a powerful technique used widely in fields like machine learning, data analysis, and natural language processing. It enables the simplification of complex data structures, reduces computational overhead, and helps reveal underlying patterns in data. Whether you are exploring concepts in a Data Science Course or applying SVD to solve real-world problems, its ability to reduce dimensionality and uncover latent features makes it invaluable. What