Closed mahdeto closed 7 years ago
One way to do this is to use the initial_config
parameter. This parameter accepts a pre-calculated embedding as a bootstrap.
The process would involve:
initial_config
).This carries some caveats, so here's my thoughts on those:
min_cost
parameter on the second pass, to give the new data a fair chance to find an optimal embedding.Keep in mind you can visualize the progress of the tsne algorithm using the epoch_callback
method. You could flag the new points, watch them settle, and get a better idea of how to optimize the parameters.
Basically the ability to embed new points (ones that I did not have at the time of training) into the map without retraining using the entire data set. A potential approach to deal with this would be to train a multivariate regressor to predict the map location from the input data. Alternatively, you could also make such a regressor minimize the t-SNE loss directly, which is what Laurens did in this paper.