2d:T4de,

In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rate of RRQR PCA by developing a nonlinear extension of RRQR PCA. In addition, a new robust RBF L p -norm kernel is proposed in order to reduce the effect of outliers and noises. Extensive experiments on two well-known standard face databases which are ORL and FERET prove that the proposed algorithm is more robust than conventional PCA, 2DPCA, PCA-L1, WTPCA-L1, LDA, and 2DLDA in terms of face recognition accuracy.

2e:T4d6,

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

2f:T500,

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε -dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

30:T6a2,