[Solved] How can you use linear algebra to reduce the dimensionality of a large dataset in SciPy? — Ans: Apply Singular Value Decomposition (SVD) to the data matrix | SciPy

SciPy - Linear Algebra (scipy.linalg)

How can you use linear algebra to reduce the dimensionality of a large dataset in SciPy?

ACalculate the determinant of the data matrix

BSort the data rows alphabetically

CApply Singular Value Decomposition (SVD) to the data matrix

DUse matrix inversion on the dataset

Step-by-Step Solution

Solution:

Step 1: Understand dimensionality reduction
Reducing dimensions means representing data with fewer variables while preserving information.
Step 2: Identify linear algebra method
SVD decomposes data matrix to find important components, reducing dimensions effectively.
Final Answer:
Apply Singular Value Decomposition (SVD) to the data matrix -> Option C
Quick Check:
Dimensionality reduction method = SVD [OK]

Quick Trick: SVD helps reduce data dimensions [OK]

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