Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
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DOI：
10.1109/icpr.2010.48
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Michal Lewandowski;J. M. D. Rincón;D. Makris;Jean-Christophe Nebel
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文献类型：
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作者：
Michal Lewandowski;J. M. D. Rincón;D. Makris;Jean-Christophe Nebel
为了有效地处理时间序列数据，提出了一种新的非线性降维方法--时间拉普拉斯特征映射。在这种基于嵌入的方法中，时间信息是目标函数所固有的，它产生了对数据点之间具有时间相干性的低维空间的描述。由于所提出的方案还包括数据和嵌入空间之间的双向映射以及关键参数的自动调整，因此它提供了与基于映射的方法相同的好处。在几个计算机视觉应用上的实验表明，该方法在精度方面优于其他降维方法。此外，其较低的计算成本和泛化能力表明它可以扩展到更大的数据集。
A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach, temporal information is intrinsic to the objective function, which produces description of low dimensional spaces with time coherence between data points. Since the proposed scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters, it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its lower computational cost and generalisation abilities suggest it is scalable to larger datasets.