Generative Semantic Hashing Enhanced via Boltzmann Machines
Table 5: Left: Retrieval precisions under different values of v with k fixed to be 10 on Reuters dataset; Right: Retrieval precision under different values of k with v fixed to be 10 on Reuters dataset.
['Value of  [ITALIC] v 0', 'Precision 0.7812', 'Value of  [ITALIC] k 1', 'Precision 0.8300']
Ranks v Low-rank perturbed covariance matrix enables the proposed model to trade-off between complexity and performance. That is, larger v allows the model to capture more dependencies among latent variables, but the required computational complexity also increases. To investigate its impacts, we evaluate the performance of the 64-bit hash codes obtained from the proposed model under different values of v, with the other key parameter k fixed to 10. Notably, the proposed model with v=0 is equivalent to NASH since there is not any correlation between the binary random variables. It can be seen that as the number of ranks increases, the retrieval precisions also increase, justifying the hypothesis that employing the posteriors with correlations can increase the model’s representational capacity and thereby improves the hash codes’ quality in turn. It is worth noting that the most significant performance improvement is observed between the models with v=0 and v=1, and then as the value of v continues to increase, the improvement becomes relatively small. This indicates that it is feasible to set the v to a relatively small value to save computational resources while retaining competitive performance.