SciPy - Linear Algebra (scipy.linalg)
You have a large dataset matrix with many features. How can SVD help reduce the dataset size while keeping most information?
You have a large dataset matrix with many features. How can SVD help reduce the dataset size while keeping most information?
hard📝 Application Q8 of 15
Step-by-Step Solution
Solution:
Step 1: Understand dimensionality reduction with SVD
SVD allows approximation of original matrix using fewer singular values and vectors.Step 2: Apply top k singular values concept
Keeping top k singular values retains most data variance, reducing size effectively.Final Answer:
By keeping only the top k singular values and corresponding vectors -> Option AQuick Check:
SVD reduces size by top k singular values [OK]
Quick Trick: Top singular values capture most data info [OK]
Common Mistakes:
MISTAKES- Removing zeros instead of reducing rank
- Sorting rows unrelated to SVD
- Normalizing data is different process
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