Abstract
In this paper, we propose the two-phase representation based classification called the two-phase linear reconstruction measure based classification (TPLRMC). It is inspired from the fact that the linear reconstruction measure (LRM) gauges the similarities among feature samples by decomposing each feature sample as a liner combination of the other feature samples with \(L_{p}\)-norm regularization. Since the linear reconstruction coefficients can fully reveal the feature’s neighborhood structure that is hidden in the data, the similarity measures among the training samples and the query sample are well provided in classifier design. In TPLRMC, it first coarsely seeks the K nearest neighbors for the query sample with LRM, and then finely represents the query sample as the linear combination of the determined K nearest neighbors and uses LRM to perform classification. The experimental results on face databases show that TPLRMC can significantly improve the classification performance.
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Acknowledgment
This work was supported by the National Science Foundation of China (Grant Nos. 61170126, 61272211), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 14KJB520007), China Postdoctoral Science Foundation (Grant No. 2015M570411) and Research Foundation for Talented Scholars of JiangSu University (Grant No. 14JDG037).
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Gou, J., Zhan, Y., Shen, X., Mao, Q., Wang, L. (2015). Two-Phase Representation Based Classification. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_26
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DOI: https://doi.org/10.1007/978-3-319-24075-6_26
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