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Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint

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Abstract

Machine learning is the modern lingo for what we’ve been trying to do for decades, namely, to make sense of the complex signals in radar and sonar and lidar and ultrasound and so forth. Deep learning is fashionable right now and those sorts of black-box approaches are effective if there is a sufficient volume and quality of training data. However, when we have appropriate physical and mathematical models of the underlying interaction of the radar, sonar, lidar, ultrasound, etc. with the materials, tissues, and/or structures of interest, it seems odd to not harness that hard-won knowledge. We explain the key issue of feature vector selection in terms of autonomously distinguishing rats from squirrels. Time–frequency analysis is introduced as a way to identify dynamic features of varmint behavior, and the dynamic wavelet fingerprint is explained as a tool to identify features from signals that may be useful for machine learning.

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Notes

  1. 1.

    Time magazine was all set to do a cover story about the importance of radar technology in the impending Allied victory, but the story got bumped off the August 20, 1945 cover by the A-bombs dropped on Japan and the end of WWII: “The U.S. has spent half again as much (nearly $3 billion) on radar as on atomic bombs. As a military threat, either in combination with atomic explosives or as a countermeasure, radar is probably as important as atomic power itself. And while the peacetime potentialities of atomic power are still only a hope, radar already is a vast going concern—a $2 billion-a-year industry, six times as big as the whole prewar radio business.”

  2. 2.

    I’ve watched the videos with the title “Rats Scamper Outside Notre Dame Cathedral as Flooding Pushes Rodents Onto Paris Streets” (January 24, 2018) but those rats are clearly scurrying. Something must have gotten lost in translation. I wonder if rats are somehow to blame for the Notre Dame fire? Surely it wasn’t squirrels nesting up in the attic!

  3. 3.

    See, for example https://www.mathworks.com/products/wavelet.html.

  4. 4.

    Those ancient humans who didn’t notice that tiger behind the bush failed to pass their genes on to us.

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  1. Williamsburg Machine Learning Algorithmics, Williamsburg, VA, 23187-0767, USA

    Mark K. Hinders

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Hinders, M.K. (2020). Background and History. In: Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Springer, Cham. https://doi.org/10.1007/978-3-030-49395-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-49395-0_1

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