16. One or more computer storage media encoded with instructions that, when executed by a processor, configure a computer to perform acts comprising: accessing a corpus of multimedia data items, the corpus of multimedia data items including positive multimedia data items and negative multimedia data items, wherein: individual positive multimedia data items of the positive multimedia data items are associated with individual labels of a plurality of labels; and the negative multimedia data items are not associated with any label of the plurality of labels; extracting a first set of features from the individual positive multimedia data items; training a classifier based at least in part on the first set of features, the classifier including a plurality of model vectors each corresponding to one of the individual labels; based at least in part on applying the classifier to one or more of the individual positive multimedia data items, collecting statistics corresponding to each of the individual labels; extracting a second set of features from a new multimedia data item; applying the classifier to the second set of features to determine similarity values corresponding to each of the individual labels; determining that the new multimedia data item is one of the negative multimedia data items; based at least in part on determining that the new multimedia data item is one of the negative multimedia data items, comparing the statistics with the similarity values corresponding to each of the individual labels; and based at least in part on comparing the statistics with the similarity values, updating individual model vectors of the plurality of model vectors.
17. One or more computer storage media as claim 16 recites, the acts further comprising: receiving a second new multimedia data item associated with a first label of the plurality of labels; extracting a third set of features from the second new multimedia data item; applying the classifier to the third set of features; based at least in part on applying the classifier to the third set of features, determining new similarity values corresponding to each of the individual labels; determining that the second new multimedia data item is one of the positive multimedia data items; determining that the classifier classified the second new multimedia data item as being associated with a second label of the plurality of labels, the second label being different from the first label; and adjusting at least two of the individual model vectors based at least in part on: scaling down a first individual model vector of the individual model vectors, the first individual model vector associated with the second label; and scaling up a second individual model vector of the individual model vectors, the second individual model vector associated with the first label.