The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms.

中文翻译：

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基于局部谱演化核的迭代滤波分解。

在当前信息时代，从日益庞大、时变、嘈杂和可能冲突的数据集中合成信息、实现理解并获得洞察力是一些最具挑战性的任务。傅里叶变换和小波多分辨率分析等传统技术不足以处理上述所有任务。经验模型分解 (EMD) 已成为解决数据处理和分析中许多具有挑战性的问题的新的强大工具。最近，已经引入了迭代滤波分解 (IFD) 来解决 EMD 的稳定性和效率问题。另一种数据分析技术是局部频谱演化内核 (LSEK)，它提供了一个具有理想时频定位的近乎完美的低通滤波器。目前的工作利用 LSEK 进一步稳定 IFD，并为信息提取、复杂性降低以及信号和图像理解提供了一种高效、灵活和稳健的方案。当前基于 LSEK 的 IFD 的性能在广泛的数据处理任务中得到了深入验证，包括模式分解、时变数据的分析、非线性动态系统的信息提取等。 所提出的 LESK 的实用性、鲁棒性和有用性基于 IFD 的大量应用得到了证明，例如股票市场数据的分析、海浪幅度的分解、生理信号的理解和噪声图像的信息恢复。将所提出方法的性能与文献中现有方法的性能进行了比较。