特征选择对FHMM性能影响研究

doi:10.3969/j.issn.1001-2400.2010.05.028

Abstract

Abstract:

In the application of the factorial hidden Markov model (FHMM) to classification and identification, feature selection is the major factor impacting the performance of FHMM. An analytical scheme is proposed based on the impact of feature selection on the performance of FHMM and a feature selection criterion for FHMM is obtained. FHMM is introduced into gait recognition. Four gait features are extracted and the experimental results of FHMM with different feature combinations are gained. McNemar's test is employed to estimate the performance of FHMM and HMM with a single feature and regularized Canonical Correlation Analysis is used to calculate the relativity between features with different dimensions. Combining the experimental results and the relativities leads to the following conclusion: the recognition performance of FHMM does not have positive connection with the features' relativity, which is greatly influenced by the performance diversity between features and the performance of recognition with the single feature. In order to make good use of FHMM's advantage, the features should be selected according to the small performance diversity between features and the good performance of the single feature. Based on this, the lower the features' relativity, the better.

Key words: factorial hidden Markov model, feature selection, regularized canonical correlation analysis, McNemar's test, gait recognition

CHEN Chang-hong;ZHAO Heng;LIANG Ji-min;JIAO Li-cheng. Influence of feature selection on FHMM[J].J4, 2010, 37(5): 934-940.

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Influence of feature selection on FHMM

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