Graph-based multi-view clustering methods have gained significant attention due to their outstanding ability of clustering-structure representation. Considering the influence on the quality of pre-constructed graphs by noise, in this paper, we propose a novel method called ANGVR. Unlike existing methods that aim to directly learn a consensus graph from multi-view data, ANGVR rebuilds the graph constructed from raw data to seek a consensus graph across views for clustering. Furthermore, to guide the construction of graph, an embedding constraint based on neighboring group structures is introduced, which explores the neighborhood structure information corresponding to neighborhood sets. The experimental results show improvements in accuracy of 5.56%, 6.77% and 4.02%, respectively on 3Sources dataset with 10%, 30% and 50% missing view compared to the existing works.

Abstract Many subspace learning methods based on low-rank representation employ the nearest neighborhood graph to preserve the local structure. However, in these methods, the nearest neighborhood graph is a binary matrix, which fails to precisely capture the similarity between distinct samples. Additionally, these methods need to manually select an appropriate number of neighbors, and they cannot adaptively update the similarity graph during projection learning. To tackle these issues, we introduce Discriminative Subspace Learning with Adaptive Graph Regularization (DSL_AGR), an innovative unsupervised subspace learning method that integrates low-rank representation, adaptive graph learning and nonnegative representation into a framework. DSL_AGR introduces a low-rank constraint to capture the global structure of the data and extract more discriminative information. Furthermore, a novel graph regularization term in DSL_AGR is guided by nonnegative representations to enhance the capability of capturing the local structure. Since closed-form solutions for the proposed method are not easily obtained, we devise an iterative optimization algorithm for its resolution. We also analyze the computational complexity and convergence of DSL_AGR. Extensive experiments on real-world datasets demonstrate that the proposed method achieves competitive performance compared with other state-of-the-art methods.

Low-rank based methods have been widely adopted to structure preserving, when the projection matrix is learned for feature extraction. However, some dilemmas still exist that degrade the classification performance: 1) The local structure of the data is ignored; 2) the reconstructed data is not consistent with the original data. To solve those problems, in this paper a weighted graph embedded low-rank projection (WGE_LRP) method is proposed. In WGE_LRP, a novel weighted graph regularization term is proposed, which can learn the local structure of the data based on the similarity of different samples. Meanwhile, an extra global information term is introduced to keep the reconstructed data consistent with the original data. Experimental results show that the proposed method can obtain competitive performance in comparison to the state-of-the-arts.

This paper defines and investigates a novel trajectory query, namely, Traversal Trajectory Aggregate (TTA) Query: Given a trajectory database and a pair of upstream and downstream spatio-temporal (ST) regions (i.e., spatial area coupled with a time interval), a TTA query aims to retrieve the total number of unique trajectories that traverse through these two ST regions. Such TTA queries play an important role in various urban applications, such as route planning, taxi dispatching, and location-based advertising. Two baselines can answer such TTA queries: (a) exact search (over the entire ST query regions) can obtain the exact answer, but it leads to extremely long running time when the ST query regions are huge; (b) uniform-sampling-based approaches estimate the query answer with sampled trajectories. However, the uniform sampling distribution may lead to significant estimation variance for TTA query, because traversal trajectories are relatively few and unevenly distributed in the query regions. To tackle these challenges, this paper proposes a novel Targeted Index Sampling (TIS) framework to answer TTA queries with high estimation accuracy. TIS employs a two-stage framework, with a Pilot Sampling Estimation (PSE) stage to estimate the distribution of trajectories in ST query region, and an Integrated Importance Sampling (IIS) stage, which collects trajectories with the importance sampling distribution obtained in PSE, and estimates the query result with an asymptotically unbiased estimator. Extensive experiments and case studies using a large-scale real taxi trajectory dataset from Shenzhen, China demonstrate that our TIS framework achieves90 percent computational time reduction over exact search, and 50 percent reduction on estimation error (with similar running time) over uniform-distribution-based sampling approaches.

Recent developments in sensors, GPS and smart phones have provided us with a large amount of mobility data. At the same time, large-scale crowd-generated social media data, such as geo-tagged tweets, provide rich semantic information about locations and events. Combining the mobility data and surrounding social media data enables us to semantically understand why a person travels to a location at a particular time (e.g., attending a local event or visiting a point of interest). Previous research on mobility data mining has been mainly focused on mining patterns using only the mobility data. In this paper, we study the problem of using social media to annotate mobility data. As social media data is often noisy, the key research problem lies in using the right model to retrieve only the relevant words with respect to a mobility record. We propose frequency-based method, Gaussian mixture model, and kernel density estimation (KDE) to tackle this problem. We show that KDE is the most suitable model as it captures the locality of word distribution very well. We test our proposal using the real dataset collected from Twitter and demonstrate the effectiveness of our techniques via both interesting case studies and a comprehensive evaluation.