You have a sparse matrix A of shape (20000, 10000) representing user-item interactions. You want to extract 100 latent features using svds. Which approach is best?
hard📝 Application Q8 of 15
SciPy - Sparse Linear Algebra
You have a sparse matrix A of shape (20000, 10000) representing user-item interactions. You want to extract 100 latent features using svds. Which approach is best?
AConvert <code>A</code> to a dense array and use <code>np.linalg.svd</code>
BConvert <code>A</code> to a sparse format and call <code>svds(A, k=100)</code>
CUse <code>svds</code> with <code>k=5000</code> to get more features
DUse <code>svds</code> without specifying <code>k</code> to get all singular values
Step-by-Step Solution
Solution:
Step 1: Confirm matrix format
Ensure A is in a sparse format (e.g., CSR) for efficient computation.
Step 2: Set k parameter
Set k=100 to extract 100 latent features.
Step 3: Avoid dense conversion
Converting to dense is memory-intensive and inefficient for large matrices.
Step 4: Avoid invalid k values
k must be less than min(A.shape), so 5000 is invalid here.
Final Answer:
A -> Option B
Quick Check:
Use sparse format and valid k for svds [OK]
Quick Trick:Keep matrix sparse and k < min(shape) [OK]
Common Mistakes:
Converting large sparse matrices to dense
Setting k larger than minimum matrix dimension
Not specifying k and expecting full SVD
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