Sure, here are a few possible introductory phrases you could use to explain the motivation behind Singular Value Decomposition (SVD) in your thesis:

1. "Singular Value Decomposition is a powerful tool for analyzing and manipulating high-dimensional data, with applications in fields ranging from image processing to machine learning."
2. "At its core, Singular Value Decomposition represents a matrix as a product of three simpler matrices, providing a compact and efficient representation of complex data structures."
3. "One of the key motivations behind Singular Value Decomposition is to reduce the dimensionality of large datasets, making them easier to visualize and analyze."
4. "Singular Value Decomposition is particularly useful in cases where the data contains noise or other sources of variability, allowing researchers to identify underlying patterns and structures."
5. "By decomposing a matrix into its singular values and vectors, Singular Value Decomposition provides a rigorous mathematical framework for understanding and manipulating complex data sets."
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