Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding

Hu, Zhanxuan; Nie, Feiping; Wang, Rong; Li, Xuelong

doi:10.1016/j.inffus.2019.09.005

Cited by 155 publications

(35 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To present the adaptation of the DBSCAN algorithm, we need the following definitions: Definition 5.1 (Human Behavior Data Neighborhoods). We define the neighborhoods of human behavioral data 𝛬 𝑖 ,  𝛬 𝑖 , for a given threshold 𝜖 by In general, solutions to sequence and human behavioral data clustering [42][43][44] are able to derive clusters with different densities. However, these algorithms do not explore the micro clusters property for anomaly detection.…”

Section: The Dbscan-cahb Algorithmmentioning

confidence: 99%

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

et al. 2021

View full text Add to dashboard Cite

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.

show abstract

Section: The Dbscan-cahb Algorithmmentioning

confidence: 99%

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Õ [122,123] . Ù ß ü ý àß K-means [124] , K-medoids [26] , Affinity Propagation [125] , Density Peak [126] , [127] Â ª Ùü õ ² ö àß, ûÍ îÑ Ë ö ½ .…”

Section: òá Aeunclassified

Video distillation

LI¹,

ZHAO²

2021

Sci. Sin.-Inf.

View full text Add to dashboard Cite

show abstract

“…In addition, the empirical evidence on real data suggests that the proposed algorithm has a stable convergence behavior. We evaluate our method on four multi-view datasets, including 3Sources [14], BBC [15], Caltech7 [16], and COIL20 [17]. The statistics of these datasets are shown in Table 1.…”

Section: Convergence Analysismentioning

confidence: 99%

Incomplete Multi-View Subspace Clustering with Low-Rank Tensor

Liu

Teng

Zhang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Incomplete multi-view clustering has attracted increasing attentions due to its superiority in partitioning unlabeled multi-view data with missing instances in real application. However, most existing methods cannot fully exploit both the view-specific and cross-view relations among data points and ignore the high-order correlations across all views. To address these issues, we propose a novel Incomplete Multi-view Subspace Clustering with Low-rank Tensor (IMSCLT) method, which could be the first tensor-based incomplete multi-view clustering method to the best of our knowledge. Specifically, the subspace representations with low-rank tensor constraint are employed to exploit both the view-specific and cross-view relations among data points and capture the high-order correlations of multiple views simultaneously. In addition, we devise a novel module which can learn a discriminative similarity graph for multi-view learning task by approximating the inner product of the view-specific and common subspace representations. Augmented Lagrangian alternative direction minimization strategy is adopted to solve the proposed IMSCLT. The experiments on several benchmark datasets demonstrate the effectiveness of IMSCLT.

show abstract

Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding

Cited by 155 publications

References 7 publications

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

Video distillation

Incomplete Multi-View Subspace Clustering with Low-Rank Tensor

Contact Info

Product

Resources

About