一种基于深度玻尔兹曼机的半监督典型相关分析算法
发布时间：2019-03-14 18:55
【摘要】：从模式分类的角度出发,针对典型相关分析(canonical correlation analysis,CCA)算法不适应于高层次关联的缺陷,提出了改进算法。将深度学习理论与典型相关分析算法相结合,基于深度玻尔兹曼机理论提出了一种半监督典型相关分析算法。通过深度玻尔兹曼机提取出样本的显层特征与隐层特征,结合已标注样本的监督信息,构造出最有效的鉴别特征。依据ORL、Yale和AR人脸数据库进行仿真实验,实验结果表明:本文算法与其他的方法相比,具有更好的识别效果。
[Abstract]:From the point of view of pattern classification, an improved (canonical correlation analysis,CCA (canonical correlation analysis) algorithm is proposed to solve the problem that it is not suitable for high-level association. Based on the deep Boltzmann machine theory, a semi-supervised canonical correlation analysis algorithm is proposed based on the combination of depth learning theory and typical correlation analysis algorithm. The most effective discriminant features are constructed by using depth Boltzmann machine to extract the explicit and hidden layer features of the samples and combine the supervised information of the labeled samples to construct the most effective discriminant features. The simulation results based on ORL,Yale and AR face database show that the proposed algorithm has better recognition performance than other methods.
【作者单位】： 郑州大学信息工程学院;
【基金】：国家自然科学基金项目(61210005;61331021)
【分类号】：TP391.41
本文编号：2440268
[Abstract]:From the point of view of pattern classification, an improved (canonical correlation analysis,CCA (canonical correlation analysis) algorithm is proposed to solve the problem that it is not suitable for high-level association. Based on the deep Boltzmann machine theory, a semi-supervised canonical correlation analysis algorithm is proposed based on the combination of depth learning theory and typical correlation analysis algorithm. The most effective discriminant features are constructed by using depth Boltzmann machine to extract the explicit and hidden layer features of the samples and combine the supervised information of the labeled samples to construct the most effective discriminant features. The simulation results based on ORL,Yale and AR face database show that the proposed algorithm has better recognition performance than other methods.
【作者单位】： 郑州大学信息工程学院;
【基金】：国家自然科学基金项目(61210005;61331021)
【分类号】：TP391.41
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