In many applications, multilabel classification involves time-series predictors, as in multilabel video classification. How to account for the temporal dependencies with respect to input variables remains an issue, especially in action learning from videos. Motivated by the problem of video categorization and captioning, we propose a nonlinear multilabel classifier based on a hidden Markov model and a weighted loss separating false positive and negative classification errors. This allows us to account for label dependence and temporal dependencies of input variables in classification. Computationally, we derive a decomposable algorithm based on block-wise coordinate descent for non-convex minimization, where it permits not only to block-wise updates but also label-wise updates, leading to scalable computation. Theoretically, we derive the Bayes rule and prove that the proposed method consistently recovers the optimal performance of the Bayes rule. In simulations, the proposed method compares favorably with its competitors ignoring either label dependence or time-dependence. Finally, the utility of the proposed method is demonstrated by an application to ActivityNet Captions dataset for understanding a video's contents.

中文翻译：

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具有多元时间序列预测器的多标签分类

在许多应用中，多标签分类涉及时间序列预测器，如多标签视频分类。如何考虑输入变量的时间依赖性仍然是一个问题，尤其是在从视频中学习动作时。受视频分类和字幕问题的启发，我们提出了一种基于隐马尔可夫模型和加权损失的非线性多标签分类器，用于分离假阳性和阴性分类错误。这允许我们考虑分类中输入变量的标签依赖性和时间依赖性。在计算上，我们推导出了一种基于块坐标下降的可分解算法，用于非凸最小化，它不仅允许按块更新，还允许按标签更新，从而实现可扩展的计算。理论上，我们推导出贝叶斯规则并证明所提出的方法始终如一地恢复了贝叶斯规则的最佳性能。在模拟中，所提出的方法与其竞争对手相比，忽略了标签依赖或时间依赖。最后，所提出方法的效用通过 ActivityNet Captions 数据集的应用程序来证明，以理解视频的内容。