Semi-supervised Partial Multi-label Feature Selection

Abstract

Multi-label feature selection is a technique for reducing feature dimensionality by filtering out a subset of features with distinguishing power from the original feature space.However,the traditional method faces the problem of labeling accuracy degradation.Real data instances are labeled with a set of candidate labels,which may include noise labels in addition to relevant labels,resulting in biased multi-label data.Existing multi-label feature selection algorithms typically assume accurate labeling of training samples or only consider missing labels.Furthermore,large-scale high-dimensional multi-labeled datasets in real situations often have only a small portion of labeled data.Therefore,this paper presents a new semi-supervised biased multi-label feature selection method.Firstly,considering the partial multi-label issue,this paper learns the true relationships between labels from samples with known labels.Then,the structural consistency between the feature space and the label space is maintained by using the stream regularization technique.Secondly,considering the label missing issue,this paper considers unlabeled data and enhance the label information by a label propagation algorithm.Additionally,considering the high-dimensional feature,this paper applies low-rank constraints to the mapping matrix to expose implicit connections between labels.It also selects features with strong distinguishing ability by introducing l_2,1 norm constraints.Experimental results demonstrate significant performance advantages of our method compared to existing semi-supervised multi-label feature selection methods.