Abstract
Canonical Correlation Analysis (CCA) is the root method in the area of multi-view representation learning, but this method does not utilize the class information of the samples. Discriminative Canonical Correlation Analysis (DCCA) is developed based on CCA which takes the class information into consideration. However, DCCA is unable to take advantages of numerous unsupervised data and may perform poorly in real-world problems. Thus, we propose a method called Manifold Regularized Discriminative Canonical Correlation Analysis for Semi-supervised Data (MRDCCA). Our algorithm can not only use labeled samples to preserve the discriminant structure, but also estimate the intrinsic geometric manifold structure of data with both labeled and unlabeled samples by introducing the Laplacian regularization terms. Experimental results on Multiple Features database and face databases show the proposed approach can provide a better recognition performance.
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Acknowledgements
This project is supported by National Natural Science Foundation of China (Grant No. 6161154034).
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Wu, H., Zhou, X. (2018). Manifold Regularized Discriminative Canonical Correlation Analysis for Semi-supervised Data. In: Deng, Z. (eds) Proceedings of 2017 Chinese Intelligent Automation Conference. CIAC 2017. Lecture Notes in Electrical Engineering, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-6445-6_19
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DOI: https://doi.org/10.1007/978-981-10-6445-6_19
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