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In this paper, we study an automatic testing system for public cloud on data security risks. To this end, we first establish a framework based on SOA (Service Oriented Architecture), which provides a capability to proceed data security tests on cloud automatically. Second, based on the framework we implement a cloud security testing system, which includes data confidentiality test and data deletion...
In the past few years, manifold learning and sparse representation have been widely used for feature extraction and dimensionality reduction. The sparse representation technique shows that one sample can be linearly recovered by the others in a data set. Based on this, sparsity preserving projections (SPP) has recently been proposed, which simply minimizes the sparse reconstructive errors among training...
Sparse representation has been extensively studied in the signal processing community, which surprisingly pointed out that one target signal can be accurately represented as a linear combination of very few measurement signals, often called atoms, in a given dictionary. This discovery has soon been employed to the field of pattern recognition and more recently, given rise to a newly developed unsupervised...
For nonlinear discrimination analysis technique, there are some key points worthy of further research. One is finding an effective rule to select appropriate kernel function parameter for different sample sets. Another is providing a simple and efficient solution for the singularity problem of within-class scatter matrix. In this paper, we focus on these two points and address a regularized nonlinear...
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