Abstract
High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application.
Original language | English |
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Article number | 19 |
Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | Symmetry |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Keywords
- Gaussian mixture model
- Generalized Bayes estimator
- Maximum likelihood estimator
- Recognition rate