In multilabel learning,the classification performance can be improved through the effective use of label correlations.However,owing to the subjectivity of manual tagging and the similarity of label semantics in practical applications,an incomplete label space is typically observed,which results in an inaccurate estimation of label correlations and thus degraded algorithm performance.Hence,a Multilabel Learning with incomplete labels using Dual-Manifold Mapping(ML-DMM)algorithm is proposed.The algorithm constructs two types of manifold mappings:feature manifold mapping,which preserves local structural information in the instance data space,and label manifold mapping,which is based on label correlations obtained through iterative learning.The algorithm first constructs a low-dimensional manifold of data through Laplace mapping and then maps the original feature space and original label space onto the low-dimensional manifold via a regression coefficient matrix and label correlation matrix,respectively.Thus,a dual-manifold mapping structure is formed to improve the algorithm performance.Finally,the regression coefficient matrix obtained via iterative learning is used for multilabel classification.Experimental results on eight multilabel datasets with three missing rates of class labels show that ML-DMM performs better than other multilabel classification methods for missing labels.
维普
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