Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
SVD will reduce the dimensionality of the data, making it easier to visualize and identify key trends in customer preferences.
B
SVD will only provide the original matrix back, as it does not transform the data in any meaningful way.
C
SVD will add more dimensions to the data, complicating the analysis of customer preferences.
D
SVD is only useful for numerical data and will not help in understanding categorical feedback.
Understanding the Answer
Let's break down why this is correct
Answer
Using SVD, the company can break the feedback matrix into three parts that reveal hidden relationships between customers and feedback features. The first part shows how each customer relates to a set of underlying “latent” factors, while the second part lists how strongly each feature is linked to those factors. By keeping only the biggest factors, the company removes random noise and focuses on the main patterns that drive most of the variation in the data. For example, if customers who rate “price” high also score “value” high, SVD will group those two features together and show which customers share that preference. This lets the company see clear preference groups, predict future feedback, and design better products.
Detailed Explanation
SVD breaks the data into three parts that capture the most important patterns. Other options are incorrect because The idea that SVD only returns the original matrix ignores that it splits the matrix into U, Σ, and V^T; SVD does not add dimensions; it actually removes less important ones.
Key Concepts
Matrix Decomposition Techniques
Data Analysis
Dimensionality Reduction
Topic
Matrix Decomposition Techniques
Difficulty
hard level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of matrix decomposition, which of the following best describes the purpose of singular value decomposition (SVD)?
easyMathematics
Practice
2
Question 2If a matrix undergoes singular value decomposition (SVD) resulting in clearer patterns in data for machine learning applications, what underlying property of the matrix is primarily responsible for this effect?
hardMathematics
Practice
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