accuracy decays with more topics

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with the following setting

import medlda
log = open("20ng_result.txt", "a")
batchsize = 512
label = 20
numword = 53975
num_sample = 100
for k in range(20, 120, 20):
        pamedlda = medlda.OnlineGibbsMedLDA(num_topic = k, labels = label, words = numword, alpha=1/k)
        pamedlda.train_with_gml('/home/wenbo/mfs/data/20ng/20ng_train.gml', batchsize)
        (pred, ind, acc) = pamedlda.infer_with_gml('/home/wenbo/mfs/data/20ng/20ng_test.gml', num_sample)
        log.write("topic: %d, batch: %d, numsample: %d, acc: %0.2f\n" % (k, batchsize, num_sample, acc))
log.close()

the results seem to differ from original paMedLDAgibbs implementation. Here the accuracy drops dramatically as the number of topics k increases.

topic: 10, batch: 512, numsample: 100, acc: 0.80
topic: 20, batch: 512, numsample: 100, acc: 0.80
topic: 30, batch: 512, numsample: 100, acc: 0.80
topic: 40, batch: 512, numsample: 100, acc: 0.80
topic: 50, batch: 512, numsample: 100, acc: 0.79
topic: 60, batch: 512, numsample: 100, acc: 0.78
topic: 70, batch: 512, numsample: 100, acc: 0.76
topic: 80, batch: 512, numsample: 100, acc: 0.72
topic: 90, batch: 512, numsample: 100, acc: 0.72
topic: 100, batch: 512, numsample: 100, acc: 0.70

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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.

[strin]

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.