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This paper considers the problem of signal decomposition and datavisualization. For this purpose, we introduce a new multiscale transform,termed `ensemble patch transformation' that enhances identification of localcharacteristics embedded in a signal and provides multiscale visualizationaccording to different levels; hence, it is useful for data analysis and signaldecomposition. In literature, there are data-adaptive decomposition methodssuch as empirical mode decomposition (EMD) by Huang et al. (1998). Along thesame line of EMD, we propose a new decomposition algorithm that extractsmeaningful components from a signal that belongs to a large class of signals,compared to the previous methods. Some theoretical properties of the proposedalgorithm are investigated. To evaluate the proposed method, we analyze severalsynthetic examples and a real-world signal.

Ensemble Patch Transformation: A New Tool for Signal Decomposition