1. A computer-implemented method 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.
1. A computer-implemented method 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. 5. A method as claim 1 recites, wherein updating the individual model vectors comprises: determining that a particular similarity value of the similarity values that corresponds to a particular individual label of the individual labels is greater than a particular statistic of the statistics associated with the particular individual label; and scaling down a particular individual model vector of the individual model vectors, the particular individual model vector corresponding to the particular individual label.