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Results tagged with svd
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user 240615
Singular value decomposition (SVD) of a matrix $\mathbf{A}$ is given by $\mathbf{A} = \mathbf{USV}^\top$ where $\mathbf{U}$ and $\mathbf{V}$ are orthogonal matrices and $\mathbf{S}$ is a diagonal matrix.
3
votes
0
answers
349
views
Why is it much quicker to compute ridge regression than regular linear regression? [duplicate]
and v are not required
I tested this out in scipy on both random and real-world data with p > n (p = 43624, n = 1750) and found ridge regression to be much quicker than ordinary linear regression and SVD … Ridge with cholesky: 3.339338541030884
Time taken to solve linear regression: 51.32710242271423
Time taken for SVD 65.02127623558044
Time taken for SVD, just s 25.550649881362915 …
- 82.8k
3
votes
1
answer
2k
views
Why is computing ridge regression with a Cholesky decomposition much quicker than using SVD?
and much quicker than just computing the diagonal matrix of the SVD. … Time taken to solve Ridge with SVD: 118.47378492355347
Time taken for SVD 92.01217150688171
Time taken for SVD, just s 44.7129647731781 …
- 3,335