📚 Learning Guide
Matrix Decomposition Techniques
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Which of the following scenarios best illustrates the application of matrix decomposition techniques in data analysis?

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Choose the Best Answer

A

Reducing the dimensions of a dataset to identify key features for a machine learning model.

B

Simply adding two matrices together to get a new matrix.

C

Creating a new matrix by multiplying a matrix with its transpose without considering its properties.

D

Performing a row operation to solve a system of linear equations.

Understanding the Answer

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Answer

Matrix decomposition lets you break a big data table into simpler, hidden parts, so you can see patterns that aren’t obvious. In data analysis, this is used when you have a huge spreadsheet of user ratings and you want to uncover groups of users and items that behave similarly. By factorizing the rating matrix into two smaller matrices, you can predict missing ratings and discover latent factors like “genre preference. ” For example, a movie recommendation system splits a user‑movie matrix into user and movie factors, allowing it to guess a rating a user has never seen. This shows how decomposition turns a complex dataset into interpretable, actionable pieces.

Detailed Explanation

Matrix decomposition breaks a big matrix into simpler pieces. Other options are incorrect because Adding matrices does not simplify or reveal hidden structure; Multiplying by a transpose ignores how to split the matrix into meaningful parts.

Key Concepts

Matrix Decomposition Techniques
Dimensionality Reduction
Data Analysis
Topic

Matrix Decomposition Techniques

Difficulty

medium level question

Cognitive Level

understand

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