====== Differences ====== This shows you the differences between two versions of the page.
latentartists [2013/06/30 16:38] ben |
latentartists [2013/06/30 17:09] (current) ben |
||
---|---|---|---|
Line 7: | Line 7: | ||
- | Assume a fixed vocabulary $V$, which in our experiments is a company internal list | + | Assume a fixed vocabulary $V$, which in our experiments is a list compiled by the Echonest |
of music related multiword terms. | of music related multiword terms. | ||
Line 52: | Line 52: | ||
$KL(\mathcal{N}_0||\mathcal{N}_1) \propto (\mathbb{E}[z_0] - \mathbb{E}[z_1])\Sigma^{-1}(\mathbb{E}[z_0] - \mathbb{E}[z_1])^T + C$ | $KL(\mathcal{N}_0||\mathcal{N}_1) \propto (\mathbb{E}[z_0] - \mathbb{E}[z_1])\Sigma^{-1}(\mathbb{E}[z_0] - \mathbb{E}[z_1])^T + C$ | ||
- | if the covariance matrix $\Sigma$ is the same for both Gaussians. This shows that if \Sigma^{-1} is a multiple | + | if the covariance matrix $\Sigma$ is the same for both Gaussians. This shows that if $\Sigma^{-1}$ is a multiple |
- | of the identity matrix, the ranking retrieved will be the same as that of Euclidian | + | of the identity matrix, the ranking retrieved will be the same as that of Euclidian distance between posterior means. |
We can calculate the artists that are similar to an arbitrary artist by calculating their distance to all other artists using one of these | We can calculate the artists that are similar to an arbitrary artist by calculating their distance to all other artists using one of these | ||
Line 66: | Line 65: | ||
an ROC curve. | an ROC curve. | ||
- | {{::1000.ps|}} | + | Our results, contained in the ROC plots below, correspond to training on the full dataset and only the top 1000 by hotttness. |
+ | In both experimental setups the same top 300 artists are used for evaluation, the only difference is the amount of information available | ||
+ | during training. | ||
+ | |||
+ | == Hottt 1000 == | ||
+ | |||
+ | {{::1000.jpg?600|}} | ||
- | {{::full.ps|}} | + | == Full Dataset == |
+ | {{::full.jpg?600|}} | ||
+ | The results do not support our hypothesis that taking uncertainty into account would create a more robust notion of similarity. | ||
+ | While both methods clearly capture the information in the Echonest artist similar lists, the area under the ROC curve is clearly | ||
+ | greater for the simple Euclidean distance based approach. | ||
+ | The reason that the experimental results do not match our intuition is unclear. One possibility is that KL divergence | ||
+ | is not an appropriate metric for similarity. | ||