  In many applications of neural network, it is common to introduce huge
amounts of input categorical features, as well as output labels. However, since
the required network size should have rapid growth with respect to the
dimensions of input and output space, there exists huge cost in both
computation and memory resources. In this paper, we present a novel method
called category coding (CC), where the design philosophy follows the principle
of minimal collision to reduce the input and output dimension effectively. In
addition, we introduce three types of category coding based on different
Euclidean domains. Experimental results show that all three proposed methods
outperform the existing state-of-the-art coding methods, such as standard
cut-off and error-correct output coding (ECOC) methods.
