Singular Value Decomposition

Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a rectangular matrix into three other matrices: a unitary matrix, a diagonal matrix of singular values, and another unitary matrix. This decomposition reveals the underlying structure of the original matrix, highlighting its principal components and allowing for dimensionality reduction and noise removal. Essentially, SVD breaks down a complex matrix into simpler, more manageable components that capture the most important information.