How is the wavelet transform used in machine learning?
A better approach for analyzing signals with a dynamical frequency spectrum is the Wavelet Transform. The Wavelet Transform has a high resolution in both the frequency- and the time-domain. It does not only tell us which frequencies are present in a signal, but also at which time these frequencies have occurred.
How is the wavelet transform expressed in scales?
Since the term frequency is reserved for the Fourier Transform, the wavelet transform is usually expressed in scales instead. That is why the two dimensions of a scaleogram are time and scale. For the ones who find frequencies more intuitive than scales, it is possible to convert scales to pseudo-frequencies with the equation
How does the wavelet transform work in time domain?
The Wavelet Transform has a high resolution in both the frequency- and the time-domain. It does not only tell us which frequencies are present in a signal, but also at which time these frequencies have occurred. This is accomplished by working with different scales.
How is a sine wave different from a wavelet transform?
The main difference is that the sine-wave is not localized in time (it stretches out from -infinity to +infinity) while a wavelet is localized in time. This allows the wavelet transform to obtain time-information in addition to frequency information.
How is the wavelet transform used in classification?
The location (frequency-value) and height (amplitude) of the peaks in the frequency spectrum then can be used as input for Classifiers like Random Forest or Gradient Boosting. This simple approach works surprisingly well for many classification problems.
How are wavelet families different from each other?
The wavelet families differ from each other since for each family a different trade-off has been made in how compact and smooth the wavelet looks like. This means that we can choose a specific wavelet family which fits best with the features we are looking for in our signal.