Truncated Singular Value Decomposition (SVD) is a dimensionality reduction technique that approximates a matrix by using only the largest singular values and their corresponding singular vectors. This method is particularly useful in data analysis as it retains the most significant features while reducing noise and computation time, making it essential for tasks like Latent Semantic Analysis and image compression.
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