This is because the feature matrix of the original data is too large, which leads to slow training and for the purpose of visualization. Therefore, sometimes data dimensionality reduction is necessary.
This is because the feature matrix of the original data is too large to cause slow training and for visualization purposes. Therefore, sometimes data degradation is necessary.
This is because the feature matrix of the original data is too large, resulting in slow training, and for the purpose of visualization. So sometimes it is necessary to reduce the dimension of data.