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Singular Value Decomposition vs Eigendecomposition for Dimensionality Reduction
Performing PCA using both methods and comparing the results
Singular value decomposition (SVD) and eigendecomposition (ED) are both matrix factorization methods that come from linear algebra.
In the field of machine learning (ML), both can be used as data reduction methods (i.e. for dimensionality reduction).
Previously, we’ve discussed eigendecomposition in detail. Today, we’ll give more emphasis on discussing SVD.
Principal component analysis (PCA) can be performed using both methods. PCA is the most popular linear dimensionality reduction technique in ML. SVD is considered as the underlying mathematics behind PCA. The popular ML library, Scikit-learn also uses SVD within its PCA()function to perform PCA. Therefore, SVD is more popular than eigendecomposition in dimensionality reduction.
NumPy provides high-level and easy-to-use functions to perform SVD and eigendecomposition.
Topics included:
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01. What is singular value decomposition?
02. SVD equation and its terms
03. Singular value decomposition in NumPy - svd() function
04. What is eigendecomposition?
05. Eigendecomposition equation and its terms
06. Eigendecomposition in…
