The Dangers of Following Trends in Research: Sparsity and Other Examples of Hammers in Search of Nails
Adali, T., Trussell, H. J., Hansen, L. K., & Calhoun, V. D. (2018, June). PROCEEDINGS OF THE IEEE, Vol. 106, pp. 1014–1018.
Trends, they are not only for the fashion industry after all. Within the engineering and computer science research communities as well, we periodically observe the phenomenon, see how certain methods suddenly start receiving particular attention, and sometimes, even though they emerge as an attractive solution for a given set of problems, they tend to become a hammer looking for new nails. At fi rst, using a new method on old problems is the natural and reasonable way to proceed. There have been remarkable successes achieved through the adoption of a tool from another fi eld or a new way of looking at old problems that brings new insights and solutions. There have been a number of such trends throughout the years in every field. In signal processing, a few notable ones include maximum entropy, wavelets, kernel methods, and the multiple up and down cycles of neural nets. A current tool that is on the rise is sparsity, more specifically solutions that promote sparsity, including coding, compressive sensing, sparse learning/estimation, sparse factorizations, and of course deep nets, which, without careful use of sparsity, would be useless. We will use sparsity as the major example in this Point of View article because it is current and illustrates the points we are making very well. While we agree that sparsity is very useful and has led to some excellent results in the past decade or so, it also allows us to address the dangers and pitfalls of blindly following trends in research. These problems are reflected in our publications and help define the overall research climate. In the following, we provide an overview on use of sparsity and then discuss a couple of specific problems.