Abstract
In this paper, we present a supervised manifold learning based dimensionality reduction method, which is titled local uncorrelated subspace learning (LUSL). In the proposed LUSL, a local margin based on point to feature space (P2S) distance metric is recommended for discriminant feature extraction. What’s more, it has been validated that the locally statistical uncorrelation is an important property to reduce the feature redundancy. Hence, a novel locally uncorrelated criterion using P2S distance metric is also put forward, which is taken to constrain the local margin. Finally, by solving both the orthogonality and the local uncorrelation constrained objective function using an iterative way, a low dimensional subspace will be explored for pattern recognition. Compared to some related subspace learning methods, the effectiveness of the proposed LUSL have been shown from experimental results on some benchmark face data sets as AR and FERET.
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Acknowledgments
This work was partly supported by the grants of National Natural Science Foundation of China (61572381, 61273303 & 61375017) and Fujian Provincial Key Laboratory of Data Intensive Computing (BD201805).
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Li, B., Wang, XH., Peng, YK., Chen, L., Ding, S. (2019). Local Uncorrelated Subspace Learning. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_1
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DOI: https://doi.org/10.1007/978-3-030-29911-8_1
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