| Functional SOM for variable-length signal windows (2009) | |||||||||||||||
Abstract | |||||||||||||||
| Abstract — Functional data are often sampled at high frequency which leads to high-dimensional vectors. The curse of dimensionality makes this type of signal difficult to handle with standard data analysis tools. Functional data analysis uses the functional nature of data to project them on a smooth basis. This paper shows how to extend functional Self-Organizing Maps (SOM) to signal windows having different lengths using functional data analysis. This technique may be applied for example on regularly sampled signals, for which the duration of each signal is varying; an example concerns electrocardiography (ECG), where the signal is usually cut according to the variable period between two heart beats. 1 | |||||||||||||||
Details der Publikation | |||||||||||||||
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