Missing multi-label learning with non-equilibrium based on two-level autoencoder

Yusheng Cheng

doi:10.1007/s10489-020-02140-1

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Missing multi-label learning with non-equilibrium based on two-level autoencoder

2021-02-22

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Yusheng Cheng *

Song Fan

Kun Qian

DOI：10.1007/s10489-020-02140-1

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Abstract

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For a multi-label learning framework, each instance may belong to multiple labels simultaneously. The classification accuracy can be improved significantly by exploiting various correlations, such as label correlations, feature correlations, or the correlations between features and labels. There are few studies on how to combine the feature and label correlations, and they deal more with complete data sets. However, missing labels or other phenomena often occur because of the cost or technical limitations in the data acquisition process. A few label completion algorithms currently suitable for missing multi-label learning, ignore the noise interference of the feature space. At the same time, the threshold of the discriminant function often affects the classification results, especially those of the labels near the threshold. All these factors pose considerable difficulties in dealing with missing labels using label correlations. Therefore, we propose a missing multi-label learning algorithm with non-equilibrium based on a two-level autoencoder. First, label density is introduced to enlarge the classification margin of the label space. Then, a new supplementary label matrix is augmented from the missing label matrix with the non-equilibrium label completion method. Finally, considering feature space noise, a two-level kernel extreme learning machine autoencoder is constructed to implement the information feature and label correlation. The effectiveness of the proposed algorithm is verified by many experiments on both missing and complete label data sets. A statistical analysis of hypothesis validates our approach.

Keywords:

Multi-label learning

Labels completion

Kernel extreme learning machine

Two-level autoencoder

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