Comments (1)
DTC is a deep clustering method, meaning that it aims at jointly optimizing the representation of data (via the autoencoder) and the clustering. Optimization is done with gradient descent (SGD) as usual in neural nets. If you look at the loss function, it is a combination of the autoencoder MSE and a KL-divergence clustering loss. For this reason, we need a clustering algorithm with parameters that can be optimized either by gradient descent (here we use a soft center-based clustering, similar to k-means but with a differentiable KL-divergence loss function), or use alternating optimization of the AE and the clustering (i.e. update only one parameter at a time).
Either way, I don't see how it could be used with agglomerative clustering because it has no straightforward loss function and cannot be optimized with SGD.
BUT you can of course use only the ConvLSTM autoencoder to first encode your data (using only reconstruction loss), and then apply agglomerative clustering on the latent representations, using any distance metric you like.
Concerning the heatmap, it is based on a supervised classification network so it needs to know the number of classes.
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Related Issues (20)
- Dimension Reduction HOT 7
- Assertion error HOT 5
- Heatmap HOT 3
- Nan: Predicted Value HOT 2
- ValueError: The name "reshape" is used 2 times in the model. All layer names should be unique. HOT 3
- CuDNNLSTM not found HOT 3
- Training and Validation Losses HOT 1
- ValueError: Input 0 is incompatible with layer AE: expected shape=(None, 5210, 6), found shape=(None, 6) HOT 3
- Problem with Autoencoder Dimensions HOT 2
- Heatmap use HOT 2
- input shape HOT 2
- how to load model.h5 HOT 2
- About the loss value HOT 2
- Heatmap issue HOT 4
- Loss interpretation
- Dependency Problems with cudnn and Tensorflow HOT 1
- Practical Use
- Requirements are hard to find out HOT 1
- variable time step HOT 4
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