IEEE Trans Image Process. 2014 Mar;23(3):1028-37. doi: 10.1109/TIP.2014.2298978.
The goal of multilabel classification is to reveal the underlying label correlations to boost the accuracy of classification tasks. Most of the existing multilabel classifiers attempt to exhaustively explore dependency between correlated labels. It increases the risk of involving unnecessary label dependencies, which are detrimental to classification performance. Actually, not all the label correlations are indispensable to multilabel model. Negligible or fragile label correlations cannot be generalized well to the testing data, especially if there exists label correlation discrepancy between training and testing sets. To minimize such negative effect in the multilabel model, we propose to learn a sparse structure of label dependency. The underlying philosophy is that as long as the multilabel dependency cannot be well explained, the principle of parsimony should be applied to the modeling process of the label correlations. The obtained sparse label dependency structure discards the outlying correlations between labels, which makes the learned model more generalizable to future samples. Experiments on real world data sets show the competitive results compared with existing algorithms.
多标签分类的目标是揭示潜在的标签相关性，以提高分类任务的准确性。现有的大多数多标签分类器都试图详尽地探索相关标签之间的依赖关系。这增加了涉及不必要的标签依赖的风险，从而不利于分类性能。实际上，并非所有的标签相关性对于多标签模型都是必不可少的。微不足道或脆弱的标签相关性不能很好地推广到测试数据中，特别是如果在训练集和测试集之间存在标签相关性差异。为了在多标签模型中最小化这种负面影响，我们提出学习标签依赖性的稀疏结构。其基本思想是，只要多标签依赖性不能很好地解释，就应该将简约原则应用于标签相关性的建模过程。获得的稀疏标签依赖结构丢弃了标签之间的异常相关性，从而使学习到的模型更具泛化能力，能够应用于未来的样本。在真实数据集上的实验结果表明，与现有算法相比具有竞争力。
