Reducing dimensions with SVD
Singular value decomposition (SVD) is a type of matrix factorization (decomposition), which can factorize matrices into two orthogonal matrices and diagonal matrices. You can multiply the original matrix back using these three matrices. SVD can reduce redundant data that is linear dependent from the perspective of linear algebra. Therefore, it can be applied to feature selection, image processing, clustering, and many other fields. In this recipe, we will illustrate how to perform dimension reduction with SVD.
Getting ready
In this recipe, we will continue using the dataset, swiss, as our input data source.
How to do it...
Perform the following steps to perform dimension reduction using SVD:
- First, you can perform
svd ...
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