I have recently published a dataset (link) with 369 classes. I ran a couple of experiments on them to get a feeling for how difficult the classification task is. Usually, I like it if there are confusion matrices to see the type of error being made. However, a $369 \times 369$ matrix is not practical.Is there a way to give the important information of big confusion matrices? For example, usually there are a lot of 0s which are not so interesting. Is it possible to sort the classes so that most non-zero entries are around the diagonal in order to allow showing multiple matrices which are part of the complete confusion matrix?Here is an example for a big confusion matrix.Examples in the WildFigure 6 of EMNIST looks nice:It is easy to see where many cases are. However, those are only $26$ classes. If the whole page was used instead of only one column this could probably be 3x as many, but that would still only be $3 \cdot 26 = 78$ classes. Not even close to 369 classes of HASY or 1000 of ImageNet.See alsoMy similar question on CS.stackexchange
How can I make big confusion matrices easier to read?
visualization;confusion matrix