通过随机方法进行可扩展的多标签预测
Scalable Multilabel Prediction via Randomized Methods
摘要：
对输出之间的依赖性进行建模是多标签分类中的一个基本挑战。在这项工作中，我们表明，一个通用的正则化非线性映射独立的预测联合预测是足以实现各种基准问题的最先进的性能。至关重要的是，我们计算联合预测，而无需获得任何独立预测，同时结合低秩和平滑正则化。我们通过利用随机算法进行矩阵分解和核近似来实现这一点。此外，我们的技术是适用于多类设置。我们将我们的方法应用于各种多类和多标签数据集，获得最先进的结果。
英文摘要：
Modeling the dependence between outputs is a fundamental challenge in multilabel classification. In this work we show that a generic regularized nonlinearity mapping independent predictions to joint predictions is sufficient to achieve state-of-the-art performance on a variety of benchmark problems. Crucially, we compute the joint predictions without ever obtaining any independent predictions, while incorporating low-rank and smoothness regularization. We achieve this by leveraging randomized algorithms for matrix decomposition and kernel approximation. Furthermore, our techniques are applicable to the multiclass setting. We apply our method to a variety of multiclass and multilabel data sets, obtaining state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
