Thanks a lot for the detailed clarification. I've always been
thinking of wavelet transforms as a "variant" of Fourier
transformation, which is also (at least supposed to be) invertible in
the continuous case.
My impression from the original query was that the author is more
interested in using for coding or data manipulation rather than just
analysis, but this may be purely due to my Fourier-based
perspective. :-)
Best,
Stefan
On 10 Jun 2006, at 19:18, Pascale Fung wrote:
> "Time frequency transformation" is basically wavelet transform.
>
> I think you are talking about discrete wavelet transform, which is
> bijective, and used for source coding purposes. I used continuous
> wavelet
> transform, which is injective, and used for recognition (or analysis)
> purposes.
>
> Discrete wavelet transform is used for coding purposes where you'd be
> concerned with recovering the original signal. Whereas in the
> application
> of bilingual word translation, I was interested in recognizing the
> patterns. I would say most NLP tasks are recognition rather than
> coding
> tasks.
>
> Nevertheless, in this particular recognition application (of bilingual
> word pair extraction) you can still recover the orginal "signal"
> from the
> output of the transformation because the output can only correspond
> to one
> and only one input.
>
> regards,
> Pascale
>
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