Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space; among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to those works exploiting low-rank label correlation. Note that multi-label is generally low-rank; low-rank label correlation is widely adopted in multi-label learning (MLL) literature. Inspired by that, we introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL. In such a way, low-rank label correlation is appropriately exploited in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL.

翻译：标签分布学习（Label Distribution Learning, LDL）是一种新颖的机器学习范式，它为每个实例分配标签分布。许多LDL方法提出在学习过程中利用标签相关性来解决指数级大小的输出空间；其中，许多方法利用了标签分布的低秩结构来捕获标签相关性。然而，近期研究揭示标签分布矩阵通常是满秩的，这给那些利用低秩标签相关性的工作带来了挑战。值得注意的是，多标签通常是低秩的；在多标签学习（Multi-Label Learning, MLL）文献中，低秩标签相关性被广泛采用。受此启发，我们在LDL中引入了一个辅助的MLL过程，并在该MLL上而非LDL中捕获低秩标签相关性。通过这种方式，我们的LDL方法中恰当地利用了低秩标签相关性。我们进行了全面的实验，结果表明我们的方法优于现有的LDL方法。此外，消融研究验证了在辅助MLL中利用低秩标签相关性的优势。