Discriminative Multiple Canonical Correlation Analysis for Multi-feature Information Fusion
Discriminative Multiple Canonical Correlation Analysis for Multi-feature Information Fusion
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DOI：
10.1109/ism.2012.15
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Lei Gao;L. Qi;E. Chen;L. Guan
中科院分区：
文献类型：
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作者：
Lei Gao;L. Qi;E. Chen;L. Guan
提出了一种新的多特征信息融合方法。该方法基于判别式多典型相关分析（DMCCA），能够从多特征信息表示中提取更多的判别特征用于识别。它表示通过最小化Frobenius范数识别的多个特征子集之间的不同模式。我们将证明典型相关分析（CCA），多重典型相关分析（MCCA）和判别典型相关分析（DCCA）是DMCCA的特殊情况。通过说话人识别和基于语音的情感识别实验，证明了DMCCA的有效性。实验结果表明，该方法优于传统的串行融合方法，CCA，MCCA和DCCA。
This paper presents a novel approach for multi-feature information fusion. The proposed method is based on the Discriminative Multiple Canonical Correlation Analysis (DMCCA), which can extract more discriminative characteristics for recognition from multi-feature information representation. It represents the different patterns among multiple subsets of features identified by minimizing the Frobenius norm. We will demonstrate that the Canonical Correlation Analysis (CCA), the Multiple Canonical Correlation Analysis (MCCA), and the Discriminative Canonical Correlation Analysis (DCCA) are special cases of the DMCCA. The effectiveness of the DMCCA is demonstrated through experimentation in speaker recognition and speech-based emotion recognition. Experimental results show that the proposed approach outperforms the traditional methods of serial fusion, CCA, MCCA and DCCA.