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The problem of multilabel classification has attracted great interest in the last decade, where each instance can be assigned with a set of multiple class labels simultaneously. It has a wide variety of real-world applications, e.g., automatic image annotations and gene function analysis. Current research on multilabel classification focuses on supervised settings which assume existence of large amounts of labeled training data. However, in many applications, the labeling of multilabeled data is extremely expensive and time consuming, while there are often abundant unlabeled data available. In this paper, we study the problem of transductive multilabel learning and propose a novel solution, called Trasductive Multilabel Classification (TraM), to effectively assign a set of multiple labels to each instance. Different from supervised multilabel learning methods, we estimate the label sets of the unlabeled instances effectively by utilizing the information from both labeled and unlabeled data. We first formulate the transductive multilabel learning as an optimization problem of estimating label concept compositions. Then, we derive a closed-form solution to this optimization problem and propose an effective algorithm to assign label sets to the unlabeled instances. Empirical studies on several real-world multilabel learning tasks demonstrate that our TraM method can effectively boost the performance of multilabel classification by using both labeled and unlabeled data. [ABSTRACT FROM AUTHOR]