PCA to reduce dimensionality to 99% variance

1. Does PCA reduce variance?
2. How much variance is acceptable in PCA?
3. How can PCA dimensionality be reduced?
4. Does PCA increase variance?

Does PCA reduce variance?

This is because PCA is designed to minimize variance (squared deviations) which is not very meaningful when performed on binary variables. If you have mixed data, alternative methods like MCA may work better.

How much variance is acceptable in PCA?

Some criteria say that the total variance explained by all components should be between 70% to 80% variance, which in this case would mean about four to five components.

How can PCA dimensionality be reduced?

Eigenvalue Decomposition and Singular Value Decomposition(SVD) from linear algebra are the two main procedures used in PCA to reduce dimensionality.

Does PCA increase variance?

Note that PCA does not actually increase the variance of your data. Rather, it rotates the data set in such a way as to align the directions in which it is spread out the most with the principal axes. This enables you to remove those dimensions along which the data is almost flat.