The vanishing ideal of points is the set of all polynomials that vanish over the points. Any vanishing ideal can be generated by a finite set of vanishing polynomials or generators, and the computation of approximate generators has been developed at the intersection of computer algebra and machine learning in the last decade under the name of approximate computation of vanishing ideals. In computer algebra, the developed algorithms are supported by theories more deeply, whereas, in machine learning, the algorithms have been developed toward applications at a cost of some theoretical properties. In this talk, I will present a review on the development of approximate computation of vanishing ideals in two fields, particularly from the perspective of the spurious vanishing problem and normalization, which are recently suggested as a new direction of development.
