Comments (2)
even for binary classification
pamedlda = medlda.OnlineGibbsMedLDA(num_topic=80, labels=2, words=61188)
pamedlda.train_with_gml('../data/binary_train.gml', batchsize=32)
(pred, ind, acc) = pamedlda.infer_with_gml('../data/binary_test.gml', num_sample=10)
acc = 0.56
for 80 topics, while acc = 0.80
for 20 topics.
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added a parameter called "stepsize", which adjust the weights for each datapoint. This parameter is set to be dataset size / batch size
in our ICML paper.
a rough explanation for this phenomenon:
Assume that K is large (say 80). after first few mini-batches, the bayesian posterior is going to be multi-modal due to uncertainty (too many parameters compared to data). therefore, for the latent samples of initial mini-batches as well as the variational approximate to be accurate, we'll set J (#latent samples per data point) to be large.
So any of the three following solutions can mitigate the accuracy denegeration:
- increase J.
- more sweeps over dataset (more than 1).
- set
stepsize
to be large.
Revisiting the binary classification experiment, the solutions lead to the following results:
- set J = 10, test accuracy = 0.76.
- set pass = 3, test accuracy = 0.81.
- set stepsize = 25, test accuracy = 0.81.
The third solution seems to be most computationally efficient one.
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