To extend the proposed method for cases where signal events are distributed in a more complex manner rather than being localized, one approach could involve employing advanced machine learning techniques that can capture intricate patterns in high-dimensional feature spaces. For instance, instead of relying solely on the assumption of localized signal events, we could utilize unsupervised learning methods such as clustering algorithms (e.g., Gaussian Mixture Models or t-SNE) to identify regions of high density that may not conform to a simple localized structure.
Additionally, incorporating deep learning architectures, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), could allow for the modeling of more complex relationships between features. These models can learn hierarchical representations of the data, potentially capturing the underlying structure of the signal distribution.
Moreover, the method could be adapted to include a multi-scale analysis, where different resolutions of the feature space are examined to identify signals at various scales. This would involve applying the low-pass filtering technique at multiple bandwidths to capture both localized and distributed signal characteristics. By integrating these approaches, the method can become more robust in identifying signals that do not conform to simple localization assumptions.