Due to feature redundancy in high dimensional data, the unsupervised feature selection methods for dimension reduction have attracted considerable attention. The current feature selection frameworks consider the global information of data, but ignore mutual screening of global and local information, and there have been no important breakthroughs on this approach research recently. We propose a novel feature selection method based on iterative optimization between the pseudo label matrix from spectral clustering and the local projection information (SNUFS), and then prove the convergence of the method. The pseudo label matrix and the local projection are designed in objective function for mutual screening and guiding the regularization feature selection by iterative approach. Our method selects features most relevant to the pseudo label and preserves the local structure of original data from feature selection matrix, where alternate iteration of different optimization items including pseudo label matrix achieve mutual screening. For this method, we give the objective function, iterative optimization fusion approach and convergence analysis in detail. Furthermore, we use K-Nearest Neighbor (KNN) and K-means to implement locality preserving projection and obtain two specific algorithms. Experiments on four real-world datasets in different fields demonstrate that our algorithms can effectively improve the accuracy of feature selection. In particular, our algorithm of KNN implementation is more effective and outperforms other major algorithms.

Abstract For the multiprocessor systems modeled by interconnection networks, one of the important properties is the characterization of fault tolerability. Connectivity, as an important parameter to evaluate fault tolerability, has witnessed research achievements. To make the evaluation more practical, conditional connectivity has been promisingly proposed. As one kind of conditional connectivity, $h$-restricted connectivity of a connected graph $G$, denoted by $\kappa ^h (G)$, is defined as the cardinality of the minimum vertex cut set $F$ such that $\delta (G-F)\geq h$. In this paper, we establish a universally $h$-restricted connectivity for a class of hypercube-based compound networks, in which the well-known networks, such as hierarchical cubic network $HCN(n, n)$ and its generalization complete cubic network $CCN(n)$, are involved.

BACKGROUND: Predicting disease-related genes is helpful for understanding the disease pathology and the molecular mechanisms during the disease progression. However, traditional methods are not suitable for screening genes related to the disease development, because there are some samples with weak label information in the disease dataset and a small number of genes are known disease-related genes. RESULTS: We designed a disease-related gene mining method based on the weakly supervised learning model in this paper. The method is separated into two steps. Firstly, the differentially expressed genes are screened based on the weakly supervised learning model. In the model, the strong and weak label information at different stages of the disease progression is fully utilized. The obtained differentially expressed gene set is stable and complete after the algorithm converges. Then, we screen disease-related genes in the obtained differentially expressed gene set using transductive support vector machine based on the difference kernel function. The difference kernel function can map the input space of the original Huntington's disease gene expression dataset to the difference space. The relation between the two genes can be evaluated more accurately in the difference space and the known disease-related gene information can be used effectively. CONCLUSIONS: The experimental results show that the disease-related gene mining method based on the weakly supervised learning model can effectively improve the precision of the disease-related gene prediction compared with other excellent methods.

Semantic location-based services (LBS) aims to provide intelligent LBS that can find and integrate various information to better meet user requirements in location-aware context. This paper argues that traditional approaches for semantic GIServices in the Cyberinfrastructure context could be extended into the LBS. After highlighting the distinguished features of semantic LBS, i.e. context semantics and location semantics, it suggests a common semantic framework that can accommodate semantics in both LBS and GIServices. A semantic-aware LBS architecture is proposed, which supports the context-aware discovery, access, composition, and use of Web information. A case study illustrates the applicability of the approach.

Provenance, the lineage of data products, has been identified as a basic research issue in distributed data and information infrastructures. Provenance could be captured at different levels of granularity. This paper investigates the granularity of provenance for both vector and raster data. In particular, it focuses on the feature and pixel level provenance, and their management in a distributed information environment. The approach is to augment existing geospatial services with provenance awareness. The results show how the provenance with different granularity can be supported in a service-oriented environment enabled by OGC services such as Web Coverage Services, Web Feature Services, Web Processing Services, thus creating a provenance-aware Geo-Cyber infrastructure.