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Exploiting label correlations is important to multi-label classification.Previous methods capture the high-order label correlations mainly bytransforming the label matrix to a latent label space with low-rank matrixfactorization. However, the label matrix is generally a full-rank orapproximate full-rank matrix, making the low-rank factorization inappropriate.Besides, in the latent space, the label correlations will become implicit. Tothis end, we propose a simple yet effective method to depict the high-orderlabel correlations explicitly, and at the same time maintain the high-rank ofthe label matrix. Moreover, we estimate the label correlations and infer modelparameters simultaneously via the local geometric structure of the input toachieve mutual enhancement. Comparative studies over twelve benchmark data setsvalidate the effectiveness of the proposed algorithm in multi-labelclassification. The exploited high-order label correlations are consistent withcommon sense empirically. Our code is publicly available athttps://github.com/Chongjie-Si/HOMI.

Multi-label Classification with High-rank and High-order Label Correlations