You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Thanks for your innovative and interesting work!
Could you please tell me why you choose (n_classes-1) components in LDA?
And can sklearn.SLIC() accept a multichannel image? (for PaviaU data set, there supposed to be 8 channels)
The text was updated successfully, but these errors were encountered:
Thanks for the questions, and
Answer to Question 1: The LDA is a supervised method that can project the raw data into a subspace according to the given training samples. Assuming that there are C classes in the training data, then the LDA could only reduce its dimension to [1, C-1], which is determined by the algorithm. To preserve as much as information, choosing the (C-1) components seems to be more reasonable.
Answer to Question 2: Yes, it can!
By the way, the LDA is not important here. You can replace it with other methods such as PCA, MNF, or even remove it if you want.
Thanks for your innovative and interesting work!
Could you please tell me why you choose (n_classes-1) components in LDA?
And can sklearn.SLIC() accept a multichannel image? (for PaviaU data set, there supposed to be 8 channels)
The text was updated successfully, but these errors were encountered: