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Sequential Subspace Clustering via Temporal Smoothness for Sequential Data Segmentation

机译:通过时间平滑度的顺序子空间聚类,用于顺序数据分割

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摘要

This paper develops a novel sequential subspace clustering method for sequential data. Inspired by the state-of-the-art methods, ordered subspace clustering, and temporal subspace clustering, we design a novel local temporal regularization term based on the concept of temporal predictability. Through minimizing the short-term variance on historical data, it can recover the temporal smoothness relationships in sequential data. Moreover, we claim that the local temporal regularization is more important than the global structural regularization for a specific task, such as sequential subspace clustering, which leads to a concise minimization objective function. To solve the bi-convex objective function, a simple and efficient optimization algorithm based on the alternate convex search method is devised to jointly learn the coding matrix and the dictionary. Furthermore, five baseline methods are also devised for comparison with our proposed method from different aspects. Extensive experimental results and comparisons with the state-of-the-art methods on three data sets demonstrate the effectiveness of the proposed temporal smoothness sequential subspace clustering method for sequential data.
机译:本文提出了一种用于顺序数据的新型顺序子空间聚类方法。受最新方法,有序子空间聚类和时间子空间聚类的启发,我们基于时间可预测性的概念设计了一个新颖的局部时间正则化术语。通过最小化历史数据的短期方差,它可以恢复顺序数据中的时间平滑度关系。此外,我们声称对于特定任务(例如顺序子空间聚类),局部时间正则化比全局结构正则化更重要,这导致了简洁的最小化目标函数。为了解决双凸目标函数,设计了一种基于交替凸搜索方法的简单高效的优化算法,以联合学习编码矩阵和字典。此外,还设计了五种基线方法,用于从不同方面与我们提出的方法进行比较。大量的实验结果以及与三个数据集上的最新技术方法的比较证明了所提出的时序平滑时序子空间聚类方法对于时序数据的有效性。

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