Heterogeneous image feature integration via multi-modal spectral clustering

Cai, Xiao; Nie, Feiping; Huang, Heng; Kamangar, Farhad

doi:10.1109/cvpr.2011.5995740

Cited by 208 publications

(139 citation statements)

References 16 publications

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“…Many previous works have studied to design an SM with high quality, such as [Cai et al, 2005]. Later, [Nie et al, 2014;Chen and Dy, 2016; proposed an ideal SM S ∈ R n×n , and introduced the CLR method .…”

Section: Graph-based Clustering Descriptionmentioning

confidence: 99%

“…The summarization of these datasets is shown in Table 1. The proposed RAMC is compared with the methods: Co-regularized spectral clustering [Kumar et al, 2011] (Co-reg), Multi-View Spectral Clustering [Cai et al, 2011] (MVSC), Robust Multi-view Spectral Clustering [Xia et al, 2014] (RMSC), Parameterweighted Multi-view Clustering (PwMC) , Self-weighted Multi-view Clustering (SwMC) . The result of CLR is also compared as a baseline in the experiment.…”

Section: Clustering Effects On Different Real Datasetsmentioning

confidence: 99%

“…[Cai et al, 2011] proposed a multi-view spectral clustering model and [Kumar et al, 2011] introduced the coregularization technique into the spectral clustering model to perform clustering tasks. However, these methods are easily affected by the poor quality view.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Auto-Weighted Multi-View Clustering

Ren

Xiao

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Multi-view clustering has played a vital role in realworld applications. It aims to cluster the data points into different groups by exploring complementary information of multi-view. A major challenge of this problem is how to learn the explicit cluster structure with multiple views when there is considerable noise. To solve this challenging problem, we propose a novel Robust Auto-weighted Multiview Clustering (RAMC), which aims to learn an optimal graph with exactly k connected components, where k is the number of clusters. 1 -norm is employed for robustness of the proposed algorithm. We have validated this in the later experiment. The new graph learned by the proposed model approximates the original graphs of each individual view but maintains an explicit cluster structure. With this optimal graph, we can immediately achieve the clustering results without any further post-processing. We conduct extensive experiments to confirm the superiority and robustness of the proposed algorithm.

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Section: Graph-based Clustering Descriptionmentioning

confidence: 99%

Section: Clustering Effects On Different Real Datasetsmentioning

confidence: 99%

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Robust Auto-Weighted Multi-View Clustering

Ren

Xiao

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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“…MVL seeks to employ multiple "independent" clues (e.g., bi-lingual information, different modalities); MKL, however, combines multiple base kernels to create a "unified" kernel for learning, where these kernels are not necessarily independent like the "views" in MVL. Similarly, both were originally studied for (semi-)supervised learning, and have been extended to unsupervised setting recently [33,6,14,15,17,16]. Multiview clustering [20,34,15,16], as an extension of MVL, assumes inherently that the views are uncorrelated.…”

Section: Related Workmentioning

confidence: 99%

Affinity aggregation for spectral clustering

Huang

Chuang

Chen

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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“…Finally, they showed that their proposed semi-supervised multi-view learning is with a substantial improvement on the classification performance than existing methods. In transfer multi-view learning, the literatures (e.g., [4,38]) leveraged the consistency of the views and considered the domain difference among the views to learn heterogenous data.…”

Section: Multi-view Learningmentioning

confidence: 99%

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

et al. 2015

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This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

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Heterogeneous image feature integration via multi-modal spectral clustering

Cited by 208 publications

References 16 publications

Robust Auto-Weighted Multi-View Clustering

Robust Auto-Weighted Multi-View Clustering

Affinity aggregation for spectral clustering

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

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