Comments (2)
Thanks! I'm glad you find openTSNE useful.
Batch gradient descent here means that we calculate the gradient (update) using all the data points, as opposed to stochastic gradient descent (SGD), where we estimate the gradient on one or a batch of data points. We're still use gradient descent, so we repeatedly calculate the gradients and update the embedding until convergence, hence the iteration.
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Okay, thank you. I just wanted to clarify that there was no notion of "epoch" or going through a batch of data points.
I guess as opposed to optimization for classical machine learning where the weights/parameter (that are being optimized) remain the same across data points (so you can have batches of data points), in the optimization here we must use the whole data because the entire data in lower dimensional space are the parameters we are trying to optimize.
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Related Issues (20)
- `latest` version of ReadTheDocs not rendering Python code HOT 4
- Switching spectral initialization to sklean.manifold.SpectralEmbeddings HOT 14
- Adding tiny amount of noise to PCA/spectral init to prevent points from overlapping
- Tutorials do not show ipynb code HOT 2
- Bug: running optimize() multiple times produces different result compared to running it once HOT 3
- Failed to install from source HOT 3
- Extend openTSNE to specific purposes HOT 4
- Barnes-Hut optimization with the default learning rate collapses on small datasets HOT 4
- Tests fail: ImportError: attempted relative import with no known parent package HOT 7
- Negative reported KL divergence for dof>1 HOT 4
- Unable to use custom callable metric HOT 2
- process crashes when /tmp gets full HOT 2
- [Windows] save TSNEEmbedding to binary, Directory error HOT 5
- Test failure on i386 HOT 9
- Cannot install on Mac M1 HOT 1
- `utils` import error in example notebooks HOT 1
- Problem with data from CSV file HOT 7
- Question on initialization HOT 4
- import errer HOT 1
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