Distance Measure Based on an Embedding of the Manifold of K-Component Gaussian Mixture Models into the Manifold of Symmetric Positive Definite Matrices
This provides a novel distance measure for GMMs, useful in machine learning applications like texture recognition, but it is incremental as it builds on existing manifold embedding techniques.
The paper tackles the problem of measuring distances between Gaussian Mixture Models (GMMs) by embedding them into a manifold of symmetric positive definite matrices, deriving a lower bound for the Fisher-Rao metric as a distance measure. It demonstrates effectiveness with accuracy results of 98%, 92%, and 93.33% on texture recognition datasets.
In this paper, a distance between the Gaussian Mixture Models(GMMs) is obtained based on an embedding of the K-component Gaussian Mixture Model into the manifold of the symmetric positive definite matrices. Proof of embedding of K-component GMMs into the manifold of symmetric positive definite matrices is given and shown that it is a submanifold. Then, proved that the manifold of GMMs with the pullback of induced metric is isometric to the submanifold with the induced metric. Through this embedding we obtain a general lower bound for the Fisher-Rao metric. This lower bound is a distance measure on the manifold of GMMs and we employ it for the similarity measure of GMMs. The effectiveness of this framework is demonstrated through an experiment on standard machine learning benchmarks, achieving accuracy of 98%, 92%, and 93.33% on the UIUC, KTH-TIPS, and UMD texture recognition datasets respectively.