The Labeled Multiple Canonical Correlation Analysis for Information Fusion

Gao, Lei; Zhang, Rui; Qi, Lin; Chen, Enqing; Guan, Ling

doi:10.1109/tmm.2018.2859590

Cited by 39 publications

(19 citation statements)

References 68 publications

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“…Viewing the table, it is evident that CDTRL yields performance superior to the others. [13] 93.33% 2DPCA [14] 94.17% LDA [15] 95.00% 2DLDA [16] 95.83% CCA [5] 95.83% DCCA [24] 98.33% LCCA [25] 96.67% 2DCCA [9] 97.50% L2DCCA [17] 98.01% The proposed CDTRL 100.00%…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

Gao

Guan

2020

IEEE Signal Process. Lett.

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As an effective tool for two-dimensional data analysis, two-dimensional canonical correlation analysis (2DCCA) is not only capable of preserving the intrinsic structural information of original two-dimensional (2D) data, but also reduces the computational complexity effectively. However, due to the unsupervised nature, 2DCCA is incapable of extracting sufficient discriminatory representations, resulting in an unsatisfying performance. In this letter, we propose a complete discriminative tensor representation learning (CDTRL) method based on linear correlation analysis for analyzing 2D signals (e.g. images). This letter shows that the introduction of the complete discriminatory tensor representation strategy provides an effective vehicle for revealing and extracting the discriminant representations across the 2D data sets, leading to improved results. Experimental results show that the proposed CDTRL outperforms state-ofthe-art methods on the evaluated data sets.

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Section: Experimental Results and Analysismentioning

confidence: 99%

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

Gao

Guan

2020

IEEE Signal Process. Lett.

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“…In this study, CCA is used for combining audiovisual features. More recently, labeled multiple canonical correlation analysis (LMCCA) is proposed for bimodal human emotion recognition [56]. They used class labels of training data to preserve the discriminative characteristics of different modalities.…”

Section: B Latent Space Fusion Methodsmentioning

confidence: 99%

A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

et al. 2019

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Multimodal emotion recognition is an emerging interdisciplinary field of research in the area of affective computing and sentiment analysis. It aims at exploiting the information carried by signals of different nature to make emotion recognition systems more accurate. This is achieved by employing a powerful multimodal fusion method. In this study, a hybrid multimodal data fusion method is proposed in which the audio and visual modalities are fused using a latent space linear map and then, their projected features into the cross-modal space are fused with the textual modality using a Dempster-Shafer (DS) theory-based evidential fusion method. The evaluation of the proposed method on the videos of the DEAP dataset shows its superiority over both decision-level and non-latent space fusion methods. Furthermore, the results reveal that employing Marginal Fisher Analysis (MFA) for feature-level audio-visual fusion results in higher improvement in comparison to cross-modal factor analysis (CFA) and canonical correlation analysis (CCA). Also, the implementation results show that exploiting textual users' comments with the audiovisual content of movies improves the performance of the system.

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“…And it is a kind of unsupervised multiple feature extraction and low-dimension features can be extracted from any two variables of a data set [37]. The objective of CCA is to study the linear relations of two vectors by maximizing correlations among them [38], [39]. Suppose X , Y are the sets of sample pairs with length N .…”

Section: B Canonical Correlation Analysis(cca)mentioning

confidence: 99%

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits. INDEX TERMS Nonlinear analog circuit fault diagnosis, WPT, RCCA-SVM.

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The Labeled Multiple Canonical Correlation Analysis for Information Fusion

Cited by 39 publications

References 68 publications

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis

A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

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