Label distribution feature selection with feature weights fusion and local label correlations

Label distribution learning, where each sample is associated with a distribution of description degree, suffers from the curse of dimensionality like other traditional learning paradigms. Feature selection as a pre-processing technique is commonly used to reduce the dimension of data. Currently, label distribution feature selection focuses on exploiting label correlations under the assumption that all samples share the same label correlations. However, the corresponding label correlations for different groups of samples may tend to be different in real-world tasks. To tackle the issue, this paper presents a novel approach named label distribution feature selection with feature weights fusion and local label correlations. First, instances are separated into several clusters regarded as local samples. Then, a two-strategy approach is presented based on the local samples. For the first, the feature weights obtained from all local samples are fused into uniform feature significance, which can effectively improve the feature discrimination. For the second, to reflect the influence of label correlations locally, a local correlation matrix is encoded via the label space of local samples as additional features. Subsequently, the feature weights for this strategy are generated from a new feature space composed of additional and original features. Finally, a subset of optimal and relevant features is selected from the top-ranked features based on feature weights obtained from the above two strategies. Extensive experiments on fifteen benchmark datasets verify that the proposed algorithm is superior to five state-of-the-art approaches in terms of six evaluation metrics.