J4 ›› 2010, Vol. 37 ›› Issue (5): 934-940.doi: 10.3969/j.issn.1001-2400.2010.05.028

• 研究论文 • 上一篇    下一篇



  1. (1. 西安电子科技大学 生命科学与技术学院生命科学研究中心,陕西 西安  710071;
    2. 南京邮电大学 通信与信息工程学院,江苏 南京  210003;
  • 收稿日期:2010-03-10 出版日期:2010-10-20 发布日期:2010-10-11
  • 通讯作者: 陈昌红
  • 作者简介:陈昌红(1982-),女,博士,E-mail: chhchen127@gmail.com.
  • 基金资助:


Influence of feature selection on FHMM

CHEN Chang-hong1,2;ZHAO Heng1;LIANG Ji-min1;JIAO Li-cheng3   

  1. (1. Research Center of Life Science, School of Life Sciences and Technolog, Xidian Univ., Xi'an  710071, China;
    2. College of Communication and Information Eng., Nanjing Univ. of Posts and Telecommunications, Nanjing  210003, China;
  • Received:2010-03-10 Online:2010-10-20 Published:2010-10-11
  • Contact: CHEN Chang-hong


在利用因子隐马尔可夫模型(Factorial Hidden Markov Model, FHMM)进行分类识别的过程中,特征选择是影响其性能的主要因素.通过研究特征选择对FHMM性能的影响,提出了一种性能分析的方案,得出了选择FHMM特征的准则.将FHMM引入到步态识别中,提取4种步态特征,得到使用不同特征组合的FHMM的实验结果.使用McNemar检验的方法将其与单个特征的识别性能做比较,结合由正则典型相关分析得到的维数不同的特征间的相关性,分析得到以下结论: 基于FHMM的识别性能与特征间的相关性并没有必然联系,其性能更多地受到特征间的识别性能差异和单个特征的识别性能的影响.为发挥FHMM的优越性,应选择特征间识别性能差异小和单个特征识别性能好的特征组合,在此基础上特征间相关性越小越好.

关键词: 因子隐马尔可夫模型, 特征选择, 正则典型相关分析, McNemar检验, 步态识别


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