Principal Component Analysis (PCA) on Convolutional Neural Network (CNN) Features

1. Can we use PCA with CNN?
2. What are the features of Principal Component Analysis?
3. Does PCA reduce the number of features?

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.

What are the features of Principal Component Analysis?

PCA is a dimensionality reduction technique that has four main parts: feature covariance, eigendecomposition, principal component transformation, and choosing components in terms of explained variance.

Does PCA reduce the number of features?

As stated earlier, Principal Component Analysis is a technique of feature extraction that maps a higher dimensional feature space to a lower-dimensional feature space. While reducing the number of dimensions, PCA ensures that maximum information of the original dataset is retained in the dataset with the reduced no.