Fast Algorithm for GMM-Based Pattern Classifier
- Gussian distribution is popularly used in statistical pattern classification problems.
Not suitabe for modeling a multi-modal distribution.
- Gussian mixture model (GMM) can approximate a multi-modal distribution and be an alternative.
Higher computational costs are not preferable.
- Statistical pattern classification problems often meet a situation that comparison between probabilities is obvious and redundant.
- In this work, an efficient implemetation of the exponential function is proposed for GMM-based pattern classification.
- A hardware friendly algorithm is obtained.
- Evaluation on programmable DSP shows the significance.
- Adaptive control of computational precision is achieved to reduce the redundant operations.
A comparison of the exponential function’s evaluation is replaced by a comparison of the intervals based on the following inequality:
where . Since is constant (1.442695040888963…), the interval calculation is achieved only by constant scaling of positive variable , flooring and bit shifts.
- Hidenori Watanabe and Shogo Muramatsu: Fast Algorithm and Efficient Implementation of GMM-Based Pattern Classifiers, Journal of Signal Processing Systems, Springer, Volume 63, Number 1, April 2011 , pp. 107-116(10), DOI: 10.1007/s11265-009-0439-z, Apr. 2011. (Online)
- Shogo Muramatsu and Hidenori Watanabe: Fast Algorithm for GMM-Based Pattern Classifier, Proc. of 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2009), pp.633-636, Taipei, Apr. 2009.
- Identification Device, Identification Method, and Identification Processing Program，Shogo Muramatsu, Hidenori Watanabe
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