Open lu77777777 opened 3 years ago
Hi lu, welcome you ask these three questions. Corresponding explanations are listed as follows.
Thank you so much for your coding sharing. I have a questions in this code, if available, could you please answer me? 1.Will directly modifying the spectral dimension value in the image_size_dict reduce the dimensionality? Or how do I set up dimensionality reduction?
Thank you so much for your coding sharing. I have a questions in this code, if available, could you please answer me? 1.Will directly modifying the spectral dimension value in the image_size_dict reduce the dimensionality? Or how do I set up dimensionality reduction?
Thank you so much for your coding sharing. I have a questions in this code, if available, could you please answer me? 1.Will directly modifying the spectral dimension value in the image_size_dict reduce the dimensionality? Or how do I set up dimensionality reduction?
Thanks for your question, please tune the 'pc' of main.py for modifying the spectral dimension. About DR, try to implement your DR algorithms in the dimension Reduction 2d of data.py.
Thank you so much for your coding sharing. I have a questions in this code, if available, could you please answer me? 1.Will directly modifying the spectral dimension value in the image_size_dict reduce the dimensionality? Or how do I set up dimensionality reduction?
Thanks for your question, please tune the 'pc' of main.py for modifying the spectral dimension. About DR, try to implement your DR algorithms in the dimension Reduction 2d of data.py. Thanks for the reply!Depending on the method you provide: Is it normal for accuracy to decrease accuracy after PCA dimensionality reduction? For example, if 10% of IP data is used, the accuracy reaches 99% without dimensionality reduction, and after the dimensionality is reduced to 30, the accuracy reaches 90%
Thank you so much for your coding sharing. I have few questions in this code, if available, could you please answer me?
Thank you so much.