Latent Tree Models for Hierarchical Topic Detection
Table 6: Per-document held-out loglikelihood on binary data: Best scores are marked in bold. The sign “-” indicates non-termination after 72 hours, and “NR” stands for “not run’.
['Time(min)', 'NIPS-1k', 'NIPS-5k', 'NIPS-10k', 'News-1k', 'News-5k', 'NYT']
In our experiments, each dataset was randomly partitioned into a training set with 80% of the data, and a test set with 20% of the data. Models were learned from the training set, and per-document loglikelihood was calculated on the held-out test set. For comparability, only the results on binary data are included. We see that HLTA-step is significantly more efficient than HLTA-batch on large datasets, while there is virtually no decrease in model quality. Note that all algorithms have parameters that control computational complexity. Thus, running time comparison is only meaningful when it is done with reference to model quality. that HLTA achieved much better model quality than the alternative algorithms using comparable or less time.