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I am doing data analysis. I used the wavelet transform and now I am trying the Hilbert–Huang transform (HHT). In the literature, I read that Hilbert–Huang transform (HHT) is an adaptive technique. I tried to find how it is adaptive or what is the meaning of adaptive in this context. I didn't get any answer.

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  • $\begingroup$ Thank you so much for your concern. I am doing analysis and soon I hope I may need your help. I will let you know soon. Thank you. $\endgroup$ – Amit Jul 4 at 8:44
  • $\begingroup$ I tried to vote but I got this warning:Thanks for the feedback! Votes cast by those with less than 15 reputation are recorded, but do not change the publicly displayed post score. $\endgroup$ – Amit Jul 24 at 13:25
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The Hilbert-Huang signal decomposition possesses a non-adaptive part (the Hilbert transform) and an adaptive, or data-driven part: the decomposition of a signal as a sum for Intrinsic Mode Functions (IMF), that are obtained by a deflation process, using extrema and envelopes of the signal, recursively.

Thus, IMFs are adaptive with respect to the signal: if you change the value of one sample, the IMFs can be drastically different. For some classes of processes, namely fractional Brownian motions, the Empirical mode decompositions [can act as] as data-driven wavelet-like expansions. In other words, under some conditions, an adaptive algorithm may behave like a non-adaptive one.

There is one code for Marginal Hilbert Spectrum on Matlab Central.

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  • $\begingroup$ Thank you so much. Very helpful.. $\endgroup$ – Amit Jun 27 at 15:45

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