document: Classification with partial labels In this paper , we address the problem of learning when some cases are fully labeled while other cases are only partially labeled , in the form of partial labels . Partial labels are represented as a set of possible labels for each training example , one of which is the correct label . We introduce a discriminative learning approach that incorporates partial label information into the conventional margin-based learning framework . The partial label learning problem is formulated as a convex quadratic optimization minimizing the L2-norm regularized empirical risk using hinge loss . We also present an efficient algorithm for classification in the presence of partial labels . Experiments with different data sets show that partial label information improves the performance of classification when there is traditional fully-labeled data , and also yields reasonable performance in the absence of any fully labeled data .
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