Pca before neural network

1. Does neural network need PCA?
2. When should you not do PCA?
3. Why is PCA used as preprocessing step before regression?
4. Can we use PCA with CNN?

Does neural network need PCA?

In principle, the linear transformation performed by PCA can be performed just as well by the input layer weights of the neural network, so it isn't strictly speaking necessary.

When should you not do PCA?

While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. Simply put, if your variables don't belong on a coordinate plane, then do not apply PCA to them.

Why is PCA used as preprocessing step before regression?

When PCA is used as part of preprocessing, the algorithm is applied to: Reduce the number of dimensions in the training dataset. De-noise the data. Because PCA is computed by finding the components which explain the greatest amount of variance, it captures the signal in the data and omits the noise.

Can we use PCA with CNN?

PCA is first applied to the two datasets to achieve dimensionality reduction. The compressed datasets are used to train the 2D-CNN and 3D-CNN models. The trained models are then used to classify the test samples.