Multi-Scale Annulus Clustering for Multi-Label Classification

Label-specific feature learning has become a hot topic as it induces classification models by accounting for the underlying features of each label. Compared with single-label annotations, multi-label annotations can describe samples from more comprehensive perspectives. It is generally believed that the compelling classification features of a data set often exist in the aggregation of label distribution. In this in-depth study of a multi-label data set, we find that the distance between all samples and the sample center is a Gaussian distribution, which means that the label distribution has the tendency to cluster from the center and spread to the surroundings. Accordingly, the double annulus field based on this distribution trend, named DEPT for double annulusfield and label-specific features for multi-label classification, is proposed in this paper. The double annulus field emphasizes that samples of a specific size can reflect some unique features of the data set. Through intra-annulus clustering for each layer of annuluses, the distinctive feature space of these labels is captured and formed. Then, the final classification model is obtained by training the feature space. Contrastive experiments on 10 benchmark multi-label data sets verify the effectiveness of the proposed algorithm.

Automated summary

Label-specific feature learning is explored in this study to account for the underlying features of each label in classification models. The authors propose a method called DEPT, which captures label-specific features based on the Gaussian distribution trend of the distance between samples and the sample center in a multi-label data set. By clustering within each layer of annuluses, the distinctive feature space for each label is formed and used to train the final classification model. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed algorithm.