Singular Value Decomposition (SVD) is a fundamental technique in linear algebra that decomposes a matrix into three other matrices, expressed as . Here, is an orthogonal matrix whose columns are the left singular vectors, is a diagonal matrix containing the singular values (which are non-negative and sorted in descending order), and is the transpose of an orthogonal matrix whose columns are the right singular vectors.
Key properties of SVD include:
Overall, the properties of SVD make it a powerful tool in various fields, including statistics, machine learning, and signal processing.
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