Abstract
Over the past years, a large family of algorithms has been designed to provide different solutions to the problem of dimensionality reduction, such as discriminant neighborhood embedding (DNE), marginal fisher analysis (MFA) and double adjacency graphs-based discriminant neighborhood embedding (DAG-DNE). In this paper, we investigate the effect of data distribution for face recognition. We conduct three settings to investigate the performance when we have different numbers of the training samples. One is randomly select 20% samples as training set and the remaining face images are used for testing. One is randomly select 40% samples as training set and the last one is randomly select 60% samples as training set. In the end, we find as interesting observation is that when the training sample size is large enough to sufficiently characterize the data distribution, all algorithms we discussed in this work can achieve good performance.
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References
Ding, C., Zhang, L.: Double adjacency graphs-based discriminant neighborhood embedding. Pattern Recogn. 48(5), 1734–1742 (2015)
Liu, Z., Wang, S., Sun, Q., Zou, H., Yang, F.: Cost-aware cloud service request scheduling for saas providers. Mob. Inf. Syst. 57(2), 291–301 (2014)
Martinez, A.M., Kak, A.C.: Pca versus lda. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)
Wang, S., Zhou, A., Hsu, C., Xiao, X., Yang, F.: Provision of data-intensive services through energy- and qos-aware virtual machine placement in national cloud data centers. IEEE Trans. Emerg. Topics Comput. 4(2), 290–300 (2016)
Wang, S., Hsu, C.-H., Liang, Z., Sun, Q., Yang, F.: Multi-user web service selection based on multi-qos prediction. Mob. Inf. Syst. 16(1), 143–152 (2014)
Wang, S., Sun, L., Sun, Q., Wei, J., Yang, F.: Reputation measurement of cloud services based on unstable feedback ratings. Int. J. Web Grid Serv. 11(4), 362–376 (2015)
Wang, S., Sun, Q., Zou, H., Yang, F.: Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. J. Intell. Manufact. 25(2), 283–291 (2014)
Wang, S., Sun, Q., Zou, H., Yang, F.: Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing. Mob. Inf. Syst. 25(2), 283–291 (2014)
Wang, S., Zheng, Z., Zhengping, W., Yang, F.: Context-aware mobile service adaptation via a co-evolution extended classifier system in mobile network environments. Mob. Inf. Syst. 10(2), 197–215 (2014)
Wang, S., Zhu, X., Sun, Q., Zou, H., Yang, F.: Low-cost web service discovery based on distributed decision tree in P2P environments. Wirel. Pers. Commun. 73(4), 1477–1493 (2013)
Wang, S., Zhu, X., Yang, F.: Efficient qos management for qos-aware web service composition. Int. J. Web Grid Serv. 10(1), 1–23 (2014)
Yan, D., Xu, S.C., Zhang, B.Y., Zhang, H.J., Yang, Q.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)
Yu, H., Yang, J.: A direct lda algorithm for high-dimensional data with application to face recognition. Pattern Recogn. 34(10), 2067–2070 (2001)
Zhang, X.Y., Xue, W., Guo, Y.F.: Discriminant neighborhood embedding for classification. Pattern Recogn. 39(11), 2240–2243 (2006)
Zhou, A., Wang, S., Li, J., Sun, Q., Yang, F.: Optimal mobile device selection for mobile cloud service providing. J. Supercomput. 72(8), 3222–3235 (2016)
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Ye, Y. (2016). Subspace Learning Based on Data Distribution for Face Recognition. In: Hsu, CH., Wang, S., Zhou, A., Shawkat, A. (eds) Internet of Vehicles – Technologies and Services. IOV 2016. Lecture Notes in Computer Science(), vol 10036. Springer, Cham. https://doi.org/10.1007/978-3-319-51969-2_20
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DOI: https://doi.org/10.1007/978-3-319-51969-2_20
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