论文标题
SRMD：稀疏随机模式分解
SRMD: Sparse Random Mode Decomposition
论文作者
论文摘要
信号分解和多尺度信号分析提供了许多有用的工具用于时频分析。我们提出了一种随机特征方法，用于通过构建少量近似频谱图来分析时间序列数据。随机化既是时间窗口的位置和频率采样，从而降低了整体采样和计算成本。频谱图的稀疏导致时频簇之间的急剧分离，从而使识别固有模式变得更加容易，从而导致了新的数据驱动模式分解。应用程序包括信号表示形式，离群值删除和模式分解。在基准测试中，我们表明我们的方法的表现优于其他最先进的分解方法。
Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis. We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram. The randomization is both in the time window locations and the frequency sampling, which lowers the overall sampling and computational cost. The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes, and thus leads to a new data-driven mode decomposition. The applications include signal representation, outlier removal, and mode decomposition. On the benchmark tests, we show that our approach outperforms other state-of-the-art decomposition methods.
