This paper considers the problem of linear Boolean classification, where the goal is to determine in which set, among two given sets of Boolean vectors, an unknown vector belongs to by making linear queries. Finding the least number of queries is equivalent to determining the minimal rank of a matrix over GF(2) , whose kernel does not intersect a given set S. In the case where $S$ is a Hamming ball, this reduces to finding linear codes of largest dimension. For a general set $S$ , this is an instance of the critical problem posed by Crapo and Rota in 1970, open in general. This paper focuses on the case where $S$ is an annulus. As opposed to balls, it is shown that an optimal kernel is composed not only of dense but also of sparse vectors, and the optimal mixture is identified in various cases. These findings corroborate a proposed conjecture that for an annulus of inner and outer radius $nq$ and $np$ respectively, the optimal relative rank is given by the normalized entropy $(1-q)H(p/(1-q))$ , an extension of the Gilbert-Varshamov bound.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences
- Linear codes
- learning theory