Theory of the GMM Kernel
Ping Li, Cun‐Hui Zhang · 2017
In web search, data mining, and machine learning, two popular measures of data similarity are the cosine and the resemblance (the latter is for binary data). In this study, we develop theoretical results for both the cosine and the GMM (generalized min-max) kernel, which is a generalization of the resemblance. GMM has direct applications in machine learning as a positive definite kernel and can be efficiently linearized via probabilistic hashing to handle big data. Owing to its discrete nature, the hashed values can also be used to build hash tables for efficient near neighbor search.