Answers.Body: <p>Even after shuffling features and labels, it seems your CNN may still capture classification relevant global features or patterns such as overall intensity distribution frequency components or inter-channel correlations that are preserved in the data despite losing spatial arrangement. For example, if your EEG data encode power spectra CNNs might be learning statistical properties of these spectra as frequency band characteristics rather than their spatial layout. If different classes have different intensity ranges or dominant frequencies, these global properties artefacts or biases can be learned by the CNN even without genuine spatial structure. In high-dimensional spaces such as your multi-channel EEG images, CNNs can still learn <em>separable</em> features even if the spatial arrangement is destroyed, because the data likely resides in a <a href="https://en.wikipedia.org/wiki/Manifold_hypothesis" rel="nofollow noreferrer">structured manifold</a>.</p>
  <p>To further investigate, you can perform PCA on the flattened EEG data before and after shuffling to check if the primary components as global patterns remain similar. If they do, the CNN might be relying on these global patterns for its classification. Also you can add random noise to remove global features and test the CNN's performance. If the accuracy drops significantly, the CNN is likely exploiting these global features. Finally you may train a fully connected MLP on the flattened data and compare its accuracy to the CNN. Similar performance would suggest the CNN is not leveraging spatial information but is acting more like a dense network.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-11-24T04:04:36.113Z
Answers.LastActivityDate: 2024-11-24T04:04:36.113Z
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Body: <p>I used multiple images to do classification. These images are multi-channel EEG data. Now I used the same random matrix to shuffle all elements on the rows and columns. I use CNN to do classification on this kind of two-dimension data and I was thinking the CNN cannot classify anymore because there is no structural/neighboring information anymore. But the results show that the CNN can still classify. The accuracy is almost the same with the original one. I checked the kernels. They are differnt. But what exactly the classifier learn?</p>
  
Tags: <deep-learning><convolutional-neural-networks>
Title: why CNN can still classify permuted images (originally multi-channel EEG data)?
UserDisplayName: Chris Lee
ThreadText: # why CNN can still classify permuted images (originally multi-channel EEG data)?
  
  (Asked by: username_0 on 2024-11-23)
  
  I used multiple images to do classification. These images are multi-channel EEG data. Now I used the same random matrix to shuffle all elements on the rows and columns. I use CNN to do classification on this kind of two-dimension data and I was thinking the CNN cannot classify anymore because there is no structural/neighboring information anymore. But the results show that the CNN can still classify. The accuracy is almost the same with the original one. I checked the kernels. They are differnt. But what exactly the classifier learn?
  
  **Tags:** deep-learning, convolutional-neural-networks
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-11-24. Score: 0
  
  Even after shuffling features and labels, it seems your CNN may still capture classification relevant global features or patterns such as overall intensity distribution frequency components or inter-channel correlations that are preserved in the data despite losing spatial arrangement. For example, if your EEG data encode power spectra CNNs might be learning statistical properties of these spectra as frequency band characteristics rather than their spatial layout. If different classes have different intensity ranges or dominant frequencies, these global properties artefacts or biases can be learned by the CNN even without genuine spatial structure. In high-dimensional spaces such as your multi-channel EEG images, CNNs can still learn *separable* features even if the spatial arrangement is destroyed, because the data likely resides in a [structured manifold](https://en.wikipedia.org/wiki/Manifold_hypothesis).
  
  To further investigate, you can perform PCA on the flattened EEG data before and after shuffling to check if the primary components as global patterns remain similar. If they do, the CNN might be relying on these global patterns for its classification. Also you can add random noise to remove global features and test the CNN's performance. If the accuracy drops significantly, the CNN is likely exploiting these global features. Finally you may train a fully connected MLP on the flattened data and compare its accuracy to the CNN. Similar performance would suggest the CNN is not leveraging spatial information but is acting more like a dense network.
  
  
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