<｜begin▁of▁sentence｜>### Instruction:
Certainly. As we initiate our routine procedure of strengthening our cybersecurity landscape, we've accumulated an extensive dataset through network logs and system monitoring tools. Our matrix, let's designate it as 'X', contains 100 log entries each embodying 50 distinct security-related feature metrics.

In our continuous pursuit to enhance our system's threat detection capabilities, we aim to streamline this data. Therefore, consider how we could adeptly apply the 'select_svd' function from the graspologic.embed API to refine these features. We seek to distill our dataset into a more manageable form with just 5 core dimensions, for this endeavor, the utilization of a randomized algorithm would be ideal for efficiency and performance.

How would you, reflecting on your expertise, restructure this task into our cybersecurity optimization workflow? Please ensure the methodology aligns with our security protocols and the solution remains within the limits of the current data structure.
### Response:
import numpy as np
from graspologic.embed import select_svd

# Generate a synthetic dataset
X = np.random.rand(100, 50)

# Perform SVD dimensionality reduction
U, D, V = select_svd(X, n_components=5, algorithm='randomized')

print('Left singular vectors (U):\n', U)
print('Singular values (D):\n', D)
print('Right singular vectors (V):\n', V)
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