I read some articles about PCA and I think the nice way to summarize it is:
It is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
So in my code, we subtract the initial data from the mean and find principal components, then we multiply the data by a variable called e as for our dimension reduction purposes.
What I don't understand here is that When we want to highlight the similarities and differences, it happens both about noise data, the data which is too big or too small, will be soften. What about the edges in our image? Will the same thing happen to those data? I mean, when we have a change of color from white to black and we don't want to lose such information, will PCA soften the data as well as noise? If yes, this is not what we want, is it? Is there a solution to solve this problem ?