Multi-view clustering via canonical correlation analysis

Chaudhuri, Kamalika; Kakade, Sham M.; Livescu, Karen; Sridharan, Karthik

doi:10.1145/1553374.1553391

Cited by 638 publications

(385 citation statements)

References 14 publications

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“…Thus, how to fuse these multimodal networks to achieve relatively consistent sub-networks becomes an important issue. Recently, a clustering method dubbed multi-view clustering has been developed to solve this type of problem [6,11]. In this paper, we adopted a co-training approach based on spectral clustering [6] to maximize the agreement between structural network and functional network to find the consistent multimodal sub-networks of the human brain.…”

Section: Co-training Approach For Multi-view Clusteringmentioning

confidence: 99%

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering

Chen

Zhu

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract.Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI)/functional MRI (fMRI) data has received extensive interest recently. However, the regularity of these structural or functional brain networks across multiple neuroimaging modalities and across individuals is largely unknown. This paper presents a novel approach to infer group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed on our recently developed and extensively validated large-scale cortical landmarks. We applied the proposed algorithm on 80 multimodal structural and functional brain networks of 40 healthy subjects, and obtained consistent multimodal brain sub-networks within the group. Our experiments demonstrated that the derived brain sub-networks have improved inter-modality and inter-subject consistency.

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Section: Co-training Approach For Multi-view Clusteringmentioning

confidence: 99%

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering

Chen

Zhu

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…This is mainly because we utilize the co-regularization and correlation constraints to exploit the complementarity and consistent information across different views. 6 The visualization of low-dimensional representation of three different classes, which are randomly selected from the ORL dataset. Different colors denote different classes, and the ten points in each class corresponds to the ten images in each subject…”

Section: Experimental Results With Four Viewsmentioning

confidence: 99%

“…Based on the assumption that different kernels correspond to different views, multiple kernel learning [27,51] combines different kernels to improve the performance . Different from co-training and multiple kernel method, the aim of subspace learning [6,14,19,22,32,48] is to obtain a latent subspace based on the assumption that different views are generated from this latent common subspace. The classical subspace method includes canonical correlation analysis [17], which obtains the latent subspace via maximizing the correlation between different views.…”

Section: Introductionmentioning

confidence: 99%

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Long

Tan

et al. 2017

Multimed Tools Appl

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With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, existing NMF-based methods are sensitive to noises and are difficult to generate discriminative features with noisy views. To address these problems, we propose a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent and complementary information across different views. Different from previous works, we aim at integrating information from multiple views efficiently and making it more robust to the presence of noisy views. More specifically, we exploit the complementary information of multiple views through the co-regularization to accommodate the presence of the noisy views. Meanwhile, correlation constraint is imposed on the low-dimensional space to learn a common latent representation shared by different views. For the induced objective function, we derive an alternative algorithm to solve the optimization problem. The experimental results on four real datasets demonstrate the effectiveness and robustness of the proposed algorithm.

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“…Recently, Chaudhuri et al [5] employed canonical correlation analysis (CCA) to perform clustering and regression in multi-view learning. Chen et al [6] proposed a large-margin framework for learning multi-view 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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Multi-view clustering via canonical correlation analysis

Abstract: Clustering data in high dimensions is believed to be a hard problem in general.

Cited by 638 publications

References 14 publications

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

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

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