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Interesting. However, a matched filter is the optimum detector wrt maximizing SNR of the output signal (the features). https://www.sciencedirect.com/topics/en

Interesting. However, a matched filter is the optimum detector wrt maximizing SNR of the output signal (the features). https://www.sciencedirect.com/topics/engineering/matched-filter https://www.sciencedirect.com/topics/engineering/matched-fil... https://en.m.wikipedia.org/wiki/Matched_filter https://en.m.wikipedia.org/wiki/Matched_filter I wonder if this technique has been tested in “noisy” signal environments? Or is this just useful in infinite SNR applications?

krapht 4y ago
optimal among linear functions, and the noise is gaussian... of course noise is not gaussian in many interesting applications and nothing is stopping a nonlinear function from getting better results than a matched filter, we just have no theory to discover what such a function would be so yes, like you, i'd need to see results on actual data before spending time to learn more about it...
- fxtentacle 4y ago
  In my opinion, this looks promising: https://github.com/stevenwalton/SCS-CCT https://github.com/stevenwalton/SCS-CCT 4% improvement over Compact Transformers, which is considered SOTA EDIT: As meragrin_ pointed out, I read the graph the wrong way. It's actually 4% worse. Sorry.
  - meragrin_ 4y ago
    Where do you see improvement? According to the graph and text, there is a 4% decrease in performance. It is slightly worse.
gnramires 4y ago
I tend to think of a matched filter as an "optimum denoiser" (in the mean squared error sense with white gaussian noise), but it's not an optimum detector in the general case, or an optimum "de-interferer". The case shown here should be more resilient to changes in signal amplitude and non-matching interference. Of course, because the kernel is actually learned, it can adapt to interference and try to be a "matched filter to what we want, plus anti-matched filter to interference" (i.e. give near-0 response to common interference signals). But SCS does seem like a more natural way to achieve a similarity metric and feature detection. (although this non-flexibility might work against it)

krapht 4y ago

optimal among linear functions, and the noise is gaussian... of course noise is not gaussian in many interesting applications and nothing is stopping a nonlinear function from getting better results than a matched filter, we just have no theory to discover what such a function would be so yes, like you, i'd need to see results on actual data before spending time to learn more about it...

fxtentacle 4y ago
In my opinion, this looks promising: https://github.com/stevenwalton/SCS-CCT https://github.com/stevenwalton/SCS-CCT 4% improvement over Compact Transformers, which is considered SOTA EDIT: As meragrin_ pointed out, I read the graph the wrong way. It's actually 4% worse. Sorry.
- meragrin_ 4y ago
  Where do you see improvement? According to the graph and text, there is a 4% decrease in performance. It is slightly worse.

gnramires 4y ago

I tend to think of a matched filter as an "optimum denoiser" (in the mean squared error sense with white gaussian noise), but it's not an optimum detector in the general case, or an optimum "de-interferer". The case shown here should be more resilient to changes in signal amplitude and non-matching interference. Of course, because the kernel is actually learned, it can adapt to interference and try to be a "matched filter to what we want, plus anti-matched filter to interference" (i.e. give near-0 response to common interference signals). But SCS does seem like a more natural way to achieve a similarity metric and feature detection. (although this non-flexibility might work against it)