Categorizing Social Multimedia by Neighborhood Decision Using Local Pairwise Label Correlation

On social media, the user generated contents, e.g., Articles and images, can be assigned with multiple labels. In this paper, we focus on the problem of performing multi-label classification on social media data, where the user generated contents are associated with multiple labels. Multi-label learning studies the problem where each object is represented by a single instance and associated with a set of labels. Current multi-label learning algorithms mainly exploit label correlations globally, by assuming that the label correlations are shared by all the examples. In real applications, however, different examples may share different label correlations. In this paper, we propose a Local Pair wise Label Correlation (LPLC) method for social media content categorization. We try to exploit the strongest local pair wise label correlations between the ground truth labels for each training example by computing the maximum conditional probabilities. If two labels have strong correlation, there will be a larger conditional probability of one label given by another. In the training stage, we find the most correlated labels for ground truth labels of each training example. In the test stage, we make prediction through maximizing the posterior probability, which is estimated with the distribution of each label in the k nearest neighbors and their most correlated local pair wise label correlations. We compare our method with six well-established multi-label learning algorithms over nine data sets from different social media data domains and scales. Comparison results with the state-of-the-arts approaches manifest competitive performances of our method.