Topology Conflict Detection Considering Incremental Updating of Multi-Scale Road Networks

Incremental updating is an important technical method used to maintain the data of road networks. Topology conflict detection of multiscale road networks in incremental updating is an important link. Most of the previous algorithms focus on a single scale road network, which cannot be applied to topology conflict detection for different scale road networks during incremental updating. Therefore, this study proposes a topology conflict detection algorithm that considers the incremental updating of multiscale networks. The algorithm designs a K-order topological neighborhood to judge incremental neighborhood links and builds a topology refinement model based on geometric measurement. Furthermore, we propose a network topology conflict detection rule considering the influence of cartographic generalization operator and use the improved topological distance to detect topology conflicts. The experimental results show that (1) the overall accuracy and recall rate of the proposed method are more than 90%; (2) after considering the topology conflict caused by cartography generalization, the accuracy was increased by 29.2%; and (3) the value of average path length of a network can be used as the basis for setting the best K value.

Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

A subspace type incremental two-dimensional principal component analysis algorithm

Principal component analysis (PCA) has been a powerful tool for high-dimensional data analysis. It is usually redesigned to the incremental PCA algorithm for processing streaming data. In this paper, we propose a subspace type incremental two-dimensional PCA algorithm (SI2DPCA) derived from an incremental updating of the eigenspace to compute several principal eigenvectors at the same time for the online feature extraction. The algorithm overcomes the problem that the approximate eigenvectors extracted from the traditional incremental two-dimensional PCA algorithm (I2DPCA) are not mutually orthogonal, and it presents more efficiently. In numerical experiments, we compare the proposed SI2DPCA with the traditional I2DPCA in terms of the accuracy of computed approximations, orthogonality errors, and execution time based on widely used datasets, such as FERET, Yale, ORL, and so on, to confirm the superiority of SI2DPCA.