Which matrix decomposition technique is commonly used in business analytics to simplify the analysis of large datasets by breaking them down into their constituent components?

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Seekh · Accepted Answer

The technique most often used is Singular Value Decomposition, or SVD. SVD takes a large data matrix and writes it as a product of three matrices, separating the data into orthogonal “basis” vectors and their associated strengths. This lets analysts see which patterns or directions explain most of the variation, making it easier to spot trends, filter noise, or compress the data. For example, a sales table can be decomposed so that a few key components explain most of the sales variation, simplifying forecasting and reporting.

Which matrix decomposition technique is commonly used in business analytics to simplify the analysis of large datasets by breaking them down into their constituent components?

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Which matrix decomposition technique is best suited for reducing dimensionality in large datasets?

Arrange the following matrix decomposition techniques in the correct order of their application for simplifying a complex matrix in data analysis: A) Eigen Decomposition, B) Singular Value Decomposition, C) Matrix Reconstruction, D) Data Interpretation.

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