Comments (2)
XifengGuo
commented on July 22, 2024
@himanshisyadav First, DEC requires the ground truth number of clusters as one of the input parameters. So I don't understand "the number of predicted classes is less than the ground truth number of classes".
Second, the objective of DEC is designed to cluster datasets with balanced clusters. The hyperspectral dataset is unbalanced (some class has many samples, some very few). So the clustering performance may be bad.
Third, I have noticed that DEC can not work well on many datasets. It proves that DEC has its own limitations. After all, we can not expect one model solves all problems.
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hammad97
commented on July 22, 2024
Well it may appear as number of predicted classes are less than ground truth when multiple cluster centroids maps on same location. Same things happen to me. That is again just DEC limitation.
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Related Issues (20)
- Question about epoch HOT 2
- Why does it get the higher Acc than that of the paper? HOT 2
- about acc HOT 1
- TypeError: add_weight() got multiple values for argument 'name' HOT 9
- loss 先升后下降 HOT 1
- Can using different autoencoder architectures help improve results? HOT 1
- What does it mean if clustering accuracy metric fluctuates a lot? HOT 1
- Conflicts to the Network Structure HOT 2
- Licence
- Difference between your reuters dataset and keras's reuters dataset HOT 1
- SAE part may be wrong. HOT 2
- cifar-10 数据集acc很低的疑问
- Suggested fix for deprecated 'from sklearn.utils.linear_assignment_ import linear_assignment' HOT 1
- How to feed TFRecord data (over 60GB) to the DEC-keras model?
- How to calculate the clusterig (KLD) loss for every instance
- ValueError: too many values to unpack (expected 2) HOT 1
- n_samples=23205 should be >= n_clusters=85617.
- Why is evaluation done on the training set
- Encoder weights are not updated during the clustering phase HOT 1
- ValueError: No such layer: clustering. HOT 2
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