multilabel learning has a wide range of potential applications in reality. it attracts a great deal of attention during the past years and has been extensively studied in many fields including image annotation and text categorization. although many efforts have been made for multilabel learning, there are two challenging issues remaining, i.e., how to exploit the correlations and how to tackle the high-dimensional problems of multilabel data. in this paper, an effective algorithm is developed for multilabel classification with utilizing those data that are relevant to the targets. the key is the construction of a coefficient-based mapping between training and test instances, where the mapping relationship exploits the correlations among the instances, rather than the explicit relationship between the variables and the class labels of data. further, a constraint, l(1)-norm penalty, is performed on the mapping relationship to make the model sparse, weakening the impacts of noisy data. our empirical study on eight public datasets shows that the proposed method is more effective in comparing with the state-of-the-art multilabel classifiers.