I've read these following papers:
- Wagner et al. (2013). Learning Convolutional Neural Networks From Few Samples.
- Ren et al. (2016). Convolutional Neural Network Based on Principal Component Analysis Initialization for Image Classification.
And they mention "PCA to initialize Convolutional Neural Network". I don't really understand, how did they do that?
PCA result is a eigenvector matrix, is it used as kernel in CNN? but does it make the kernel size is too big? or is it used as feature map?
I hope somebody can help explain to me.