论文标题
具有高级和高级标签相关性的多标签分类
Multi-label Classification with High-rank and High-order Label Correlations
论文作者
论文摘要
利用标签相关性对于多标签分类很重要。以前的方法主要通过将标签矩阵转换为具有低级矩阵分解的潜在标签空间，捕获了高阶标签相关性。但是，标签矩阵通常是一个全等级或近似的全级矩阵，使得低级分解不合适。此外，在潜在空间中，标签相关将成为隐式。为此，我们提出了一种简单而有效的方法，以明确描绘高阶标签相关性，同时保持标签矩阵的高级别。此外，我们通过输入的局部几何结构同时估算标签相关性和推断模型参数，以实现相互增强。超过十二个基准数据集的比较研究验证了所提出的算法在多标签分类中的有效性。被剥削的高阶标签相关性与常识在经验上是一致的。我们的代码可在https://github.com/chongjie-si/homi上公开获取。
Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. Our code is publicly available at https://github.com/Chongjie-Si/HOMI.
