I read this question on Kullback Liebler Divergence
Now i'm have a multidimensional distributions, like these:
for example i try to predict if a person in image is a male:
sample(img) | P_true | P_pred | | | n/a |no | yes | n/a | no | yes A | 0 | 1 | 0 | 0.2 | 0.6 | 0.2 B | 0 | 0 | 1 | 0.1 | 0.0 | 0.9 C | 1 | 0 | 0 | 0.4 | 0.2 | 0.4
where A,B,C are my examples, P_true is the groundtruth (the correct labels) and P_pred, n/a is the label that i can't say if is a male or not.
The p_pred are estimated probability vector (obtained by a softmax).
what is the formula for compute KL divergence with multidimensional probability vector?