Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

doi:10.1109/access.2019.2910322

Cited by 19 publications

(2 citation statements)

References 35 publications

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“…Several approaches have been proposed over the years for the analysis of multi-view data. They include techniques on clustering [Ye et al, 2018, Ou et al, 2018, classification [Shu et al, 2019], regression [Li et al, 2019], dimensionality reduction [Sun, 2013] and more [Xu et al, 2013, Zhao et al, 2017. However, the literature mostly lacks efficient algorithms that allow the construction of a single visualisation, through the simultaneous analysis of multiple data-views.…”

Section: Introductionmentioning

confidence: 99%

Multi-view Data Visualisation via Manifold Learning

Rodosthenous¹,

Shahrezaei²,

Evangelou³

2021

Preprint

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Manifold learning approaches, such as Stochastic Neighbour Embedding (SNE), Locally Linear Embedding (LLE) and Isometric Feature Mapping (ISOMAP) have been proposed for performing non-linear dimensionality reduction. These methods aim to produce two or three latent embeddings, in order to visualise the data in intelligible representations. This manuscript proposes extensions of Student's t-distributed SNE (t-SNE), LLE and ISOMAP, to allow for dimensionality reduction and subsequent visualisation of multi-view data. Nowadays, it is very common to have multiple data-views on the same samples. Each data-view contains a set of features describing different aspects of the samples. For example, in biomedical studies it is possible to generate multiple OMICS data sets for the same individuals, such as transcriptomics, genomics, epigenomics, enabling better understanding of the relationships between the different biological processes. Through the analysis of real and simulated datasets, the visualisation performance of the proposed methods is illustrated. Data visualisations have been often utilised for identifying any potential clusters in the data sets. We show that by incorporating the low-dimensional embeddings obtained via the multi-view manifold learning approaches into the K-means algorithm, clusters of the samples are accurately identified. Our proposed multi-SNE method outperforms the corresponding multi-ISOMAP and multi-LLE proposed methods. Interestingly, multi-SNE is found to have comparable performance with methods proposed in the literature for performing multi-view clustering.

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Section: Introductionmentioning

confidence: 99%

Multi-view Data Visualisation via Manifold Learning

Rodosthenous¹,

Shahrezaei²,

Evangelou³

2021

Preprint

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“…However, compatible and complementary information across all views can be typically underutilized. Recently, numerous multi-view clustering approaches [24,25,26,27,28,29] have been available. Bickel and Scheffer [30] extended the classic K-means and expectation-maximization clustering methods to the multi-view case to deal with text data with two views.…”

Section: Introductionmentioning

confidence: 99%

Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering

Lina,

Zhongb,

Shu

2020

Preprint

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Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance. It remains challenging to effectively exploit complementary information across multiple views since the original data often contain noise and are highly redundant. Moreover, most existing multi-view clustering methods only aim to explore the consistency of all views while ignoring the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because different views would present different geometric structures while admitting the same cluster structure. To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. Especially, a common cluster structure regularization is adopted to guarantee consistency among different views. An efficient algorithm based on an augmented Lagrangian multiplier is also developed to solve the associated optimization problem. Experiments conducted on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.

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DMVMLC-VT: Deep incomplete multi-view multi-label image classification with view translation and pseudo-label enhancement

Liu,

Zhu

2024

Vis Comput

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Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

Cited by 19 publications

References 35 publications

Multi-view Data Visualisation via Manifold Learning

Multi-view Data Visualisation via Manifold Learning

Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering

DMVMLC-VT: Deep incomplete multi-view multi-label image classification with view translation and pseudo-label enhancement

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