Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data

Zhao, Liang; Yang, Tao; Zhang, Jie; Chen, Zhikui; Yang, Yi; Wang, Z. Jane

doi:10.1109/tnnls.2020.2984810

Cited by 40 publications

(19 citation statements)

References 28 publications

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“…In our experiments, we set

α = 15

β = 0.01

and the dimension of view‐specific feature

m_{v} = \frac{m_{c}}{3}

, which is given in [18]. CoUFC [35]: a non‐incremental algorithm for multi‐view correlated and uncorrelated feature learning. We use the default parameter settings in [35].…”

Section: Methodsmentioning

confidence: 99%

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Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Zhao

Yang

Zhang

et al. 2021

IET Computer Vision

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In real‐world applications, large amounts of data from multiple sources come in the form of streams. This makes multi‐view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi‐view data becomes a challenging problem. Some single‐view methods focus on processing the data dynamically, but they are not suitable for multi‐view data. Some online multi‐view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi‐view Correlated Feature Learning (IMCFL) based on non‐negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi‐view common feature learning. Extensive experiments on real‐world datasets confirm its superiority by comparing it with other state‐of‐the‐art incremental and non‐incremental methods.

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“…In our experiments, we set

α = 15

β = 0.01

and the dimension of view‐specific feature

m_{v} = \frac{m_{c}}{3}

, which is given in [18]. CoUFC [35]: a non‐incremental algorithm for multi‐view correlated and uncorrelated feature learning. We use the default parameter settings in [35].…”

Section: Methodsmentioning

confidence: 99%

“… CoUFC [35]: a non‐incremental algorithm for multi‐view correlated and uncorrelated feature learning. We use the default parameter settings in [35]. IMCFL : an incremental algorithm proposed in this study.…”

Section: Methodsmentioning

confidence: 99%

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Zhao

Yang

Zhang

et al. 2021

IET Computer Vision

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“…SC is the method that performs the spectral clustering on the initial values of (S + S T )/2 of the proposed method. Three widely used evaluation metrics are selected to measure the clustering performance, including Normalized Mutual Information (NMI), Accuracy (ACC), and Purity [21]. Larger value of these metrics indicates better performance.…”

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)

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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.

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“…Improved deep embedded clustering (IDEC) improves clustering performance by preserving the local structure of data [25]. Colearning nonnegative correlated and uncorrelated features (CoUFC) [26] recognizes view-specific features and eliminates the influence of irrelevant information to obtain useful interview feature correlation.…”

Section: Related Workmentioning

confidence: 99%

Semisupervised Deep Embedded Clustering with Adaptive Labels

Chen

Gao

et al. 2021

Scientific Programming

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Deep embedding clustering (DEC) attracts much attention due to its outperforming performance attributed to the end-to-end clustering. However, DEC cannot make use of small amount of a priori knowledge contained in data of increasing volume. To tackle this challenge, a semisupervised deep embedded clustering algorithm with adaptive labels is proposed to cluster those data in a semisupervised end-to-end manner on the basis of a little priori knowledge. Specifically, a deep semisupervised clustering network is designed based on the autoencoder paradigm and deep clustering, which well mine the clustering representation and clustering assignment by preventing the shift of labels in DEC. Then, to train parameters of the deep semisupervised clustering network, a back-propagation-based algorithm with adaptive labels is introduced based on the pretrain and fine-tune strategies. Finally, extensive experiments on representative datasets are conducted to evaluate the performance of the proposed method in terms of clustering accuracy and normalized mutual information. Results show the proposed method outperforms the state-of-the-art methods of DEC.

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Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data

Cited by 40 publications

References 28 publications

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

Incomplete Multi-View Subspace Clustering with Low-Rank Tensor

Semisupervised Deep Embedded Clustering with Adaptive Labels

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