1. A method comprising: Receiving a query to search for multimedia content; Identifying items of multimedia content, the identified items determined to be likely relevant to the received query; In response to the receiving, providing the identified items of multimedia content; Obtaining data indicative of positive matches and negative matches from the identified items of multimedia content, a positive match indicative that an identified item likely matches a result to the search query, a negative match indicative that an identified item likely does not match the result to the search query, wherein the positive matches and the negative matches are selected based on distances obtained by mapping the data to a Euclidean space; Iteratively training a system to provide the result of the search, wherein, in a first iteration, the system is trained based on the positive matches and the negative matchers selected based on the distances obtained by mapping the data to the Euclidean space; Wherein information that is learned from the training is arranged into a multi-dimensional feature vector having components with labels, wherein a marginal probability of each label for each component of the feature vector is defined as 
 { P ( y=+ 1|χ 1 ), P ( y=− 1|χ 1 )}. Where x 1 is the l-th component of the feature vector; Wherein the probabilities 
 { P ( y=+ 1|χ 1 ), P ( y=− 1|χ 1 )}. Are estimated by counting a number of matches that fall in each bin 
 { P ( y=+ 1|χ 1 ), P ( y=− 1|χ 1 )}. Where the indicator function l(.) takes value one when its argument is true and zero otherwise. l is the number of labeled training data, X il the l-th component of training vector x i , Δ lk is the size of quantization interval along dimension l centered at reconstruction value r lk ; Obtaining a non-parametric model of statistics based on information divergence.