Subspace segmentation-based robust multiple kernel clustering

Zhou, Sihang; Zhu, En; Liu, Xinwang; Zheng, Tianming; Liu, Qiang; Xia, Jingyuan; Yin, Jianping

doi:10.1016/j.inffus.2019.06.017

Cited by 52 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-view clustering aims to utilize the features of multiple views to achieve a unified clustering result. In recent years, many multi-view clustering methods have been designed from different technical perspectives [2,3,5,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. Some important categories in multi-view clustering include the co-training based methods [13,14,15], the multi-kernel based methods [16,17,19,20], the graph learning based methods [23,24,25,26], and the subspace learning based methods [2,3,5].…”

Section: Related Workmentioning

confidence: 99%

Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering

Cai¹,

Dong²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of the previous multi-view subspace clustering algorithms are still faced with two limitations. First, they usually focus on the consistency (or commonness) of multiple views, yet often lack the ability to capture the cross-view inconsistencies in subspace representations. Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple local structures to enhance the subspace representation learning. To address these two limitations, in this paper, we propose a jointly smoothed multi-view subspace clustering (JSMC) approach. Specifically, we simultaneously incorporate the cross-view commonness and inconsistencies into the subspace representation learning. The viewconsensus grouping effect is presented to jointly exploit the local structures of multiple views to regularize the view-commonness representation, which is further associated with the low-rank constraint via the nuclear norm to strengthen its cluster structure. Thus the cross-view commonness and inconsistencies, the view-consensus grouping effect, and the low-rank representation are seamlessly incorporated into a unified objective function, upon which an alternating optimization algorithm is performed to achieve a robust subspace representation for clustering. Experimental results on a variety of real-world multi-view datasets have confirmed the superiority of the proposed approach.

show abstract

Section: Related Workmentioning

confidence: 99%

Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering

Cai¹,

Dong²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…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

“…Co-training method is a collabora-tive learning approach which usually involves two successive stages by firstly applying different algorithms to each view and secondly joining the separate results together [5]- [10]. The second category is multiple kernel clustering algorithm, which employs different kernels to introduce non-linearity to the clustering process [11], [12]. The difficulty of this method is to combine different kernels, with which [13]- [15] and [16] deal by matrix-induced regularization and late-fusion alignment, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multiple Kernel k-Means With Low-Rank Neighborhood Kernel

Gao

Zhu

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view. Most of the existing methods aim to construct a consensus kernel for the afterward clustering. However, they ignore that the desired kernel is supposed to reveal the cluster structure among samples and thus to be low rank. As a consequence, the corresponding clustering performance could decrease. To address this issue, we propose a low-rank kernel learning approach for multiple kernel clustering. Specifically, instead of regularizing the consensus kernel with low-rank constraints, we use a re-parameterize scheme for the kernel matrix. Meanwhile, the consensus kernel is located in the neighborhood area of the linear combination of base kernels. An alternate optimization strategy is designed to solve the resulting optimization problem. We evaluate the proposed method on 13 benchmark datasets with 9 state-of-the-art algorithms. As is demonstrated by experimental results, our proposed algorithm achieves superior clustering scores against the compared algorithms on the reported popular multiple kernel datasets.

show abstract

Subspace segmentation-based robust multiple kernel clustering

Cited by 52 publications

References 22 publications

Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering

Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering

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

Multiple Kernel k-Means With Low-Rank Neighborhood Kernel

Contact Info

Product

Resources

About