Discriminative Multiple Canonical Correlation Analysis for Information Fusion

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

doi:10.1109/tip.2017.2765820

Cited by 77 publications

(33 citation statements)

References 49 publications

(61 reference statements)

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“…Correlation RT Class info. Intra-view Inter-view FT Pairwise Tensor LP DA LP DA MCCA [17] RMCCA [18] TCCA [27] LapMCCs [26] GrMCCs [39] sMVCCA [28] DMCCA [32] SFEMCCA [30] SFGMCCA sMVCCA, DMCCA and SFEMCCA. As shown in the table, the checkmark " " is put in accordance with the kind of correlation (i.e., pairwise or high order), use of the regularization term, class information, locality structure preservation and discriminant analysis in intra-view and inter-view, and the fractional-order technique.…”

Section: Methodsmentioning

confidence: 99%

“…LPbSCCA introduces a label propagation algorithm based on sparse representation to infer label information for unlabeled data. Furthermore, discriminative multiple canonical correlation analysis (DMCCA) [32], which can be constructed by adding the LDA-based approach to the MCCA-based approach was proposed. From the above, several unique and interesting supervised CCA-based methods for multi-view data have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

et al. 2020

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Techniques for integrating different types of multiple features effectively have been actively studied in recent years. Multiset canonical correlation analysis (MCCA), which maximizes the sum of pairwise correlations of interview (i.e., between different features), is one of the powerful methods for integrating different types of multiple features, and various MCCA-based methods have been proposed. This work focuses on a supervised MCCA variant in order to construct a novel effective feature integration framework. In this paper, we newly propose supervised fractional-order embedding geometrical multiview CCA (SFGMCCA). This method constructs not only the correlation structure but also two types of geometrical structures of intra-view (i.e., within each feature) and interview simultaneously, thereby realizing more precise feature integration. This method also supports the integration of small sample and high-dimensional data by using the fractional-order technique. We conducted experiments using four types of image datasets, i.e., MNIST, COIL-20, ETH-80 and CIFAR-10. Furthermore, we also performed an fMRI dataset containing brain signals to verify the robustness. As a result, it was confirmed that accuracy improvements using SFGMCCA were statistically significant at the significance level of 0.05 compared to those using conventional representative MCCA-based methods. INDEX TERMS Feature integration, multi-view, canonical correlation analysis, fractional-order technique, geometrical structure.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

et al. 2020

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“…Analyzing random errors in inertial sensors is feasible for improving the accuracy of inertial navigation systems. The traditional methods of analyzing random errors include the power spectral density [ 36 ], autocorrelation analysis [ 37 ], and the Allan variance [ 38 ]. The Allan variance is widely used because it is able to distinguish different error sources and can be calculated and separated easily.…”

Section: Methodsmentioning

confidence: 99%

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Fan

Zhang

Sun

et al. 2018

Sensors

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Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value.

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“…In order to reduce the energy consumption of the redundant data transmissions, many people compressed the detected data through information fusion technology in cluster heads. 26,27 The transport model is shown in Figure 1(b). Figure 2 shows the methodology of our proposed model.…”

Section: Model and Algorithm Of Slstmmentioning

confidence: 99%

Sparse long short-term memory for information fusion in wireless sensor networks

Zhou

Zhang

Xie

et al. 2019

International Journal of Distributed Sensor Networks

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Wireless sensor networks are designed to perceive, gather, and process external environmental information and send it to the observer. However, the transmission of mass information is a challenge to the sensor nodes. To address this challenge, information fusion technologies are proposed to reduce mass redundant data. However, these techniques rarely consider the historical information, and thereby often encounter the difficulty of low prediction accuracy. In order to solve this difficulty, we propose a novel information fusion approach for the cluster heads. The proposed approach is based on time-recurrent neural network, called sparse long short-term memory, which is derived from the long shortterm memory network. The sparse long short-term memory uses sparse matrix to reduce the dimension for a highdimensional coefficient matrix. Therefore, the computational cost of the fusion algorithm is reduced in wireless sensor networks. The simulation results show that the sparse long short-term memory algorithm increases the survival number of sensor nodes in wireless sensor networks. Furthermore, the prediction accuracy of the sparse long short-term memory algorithm is almost the same as the other comparison algorithms.

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Discriminative Multiple Canonical Correlation Analysis for Information Fusion

Cited by 77 publications

References 49 publications

Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

Sparse long short-term memory for information fusion in wireless sensor networks

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