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

Ou, Weihua; Long, Fei; Tan, Yi; Yu, Shujian; Peng-peng, Wang

doi:10.1007/s11042-017-4926-0

Cited by 28 publications

(4 citation statements)

References 48 publications

(43 reference statements)

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“…Most existing multiview clustering methods can be classified into three major categories, namely, multiview spectral clustering methods, 8,9,[11][12][13] multiview subspace clustering methods, 7,[19][20][21][22][23] and multiview nonnegative matrix factorization clustering methods. [24][25][26][27][28][29] Although the three categories of methods are based on diverse theories, they have the same main idea, in other words, all these methods are designed to combine information from multiview data (or representations) into a common representation, pursuing the consensus information among all views. 6,9,27,30 The difference among these methods lies in terms of the way in which the multiview information is integrated.…”

Section: Related Workmentioning

confidence: 99%

Multiview spectral clustering via complementary information

Liu

Zheng

et al. 2020

Concurrency and Computation

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In this article, multiview spectral clustering via complementary information (MSCC) is proposed, in which both the consensus information and the complementary information are explored for multiview clustering. In contrast to most multiview spectral clustering methods, the proposed MSCC considers the differences among multiple views and constructs a similarity matrix for clustering. Furthermore, a convex relaxation is employed and an algorithm that is based on the augmented Lagrange multiplier is proposed for optimizing the objective function of MSCC. In extensive experiments on five real‐world benchmark datasets, our proposed method outperforms two baselines and has significantly improved to several state‐of‐the‐art multiview clustering methods.

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Section: Related Workmentioning

confidence: 99%

Multiview spectral clustering via complementary information

Liu

Zheng

et al. 2020

Concurrency and Computation

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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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“…Consistency represents the common information of multiple views, and complementarity represents the special information of each view. Only consistency and complementarity of multiple views can be utilized to improve the performance of learning tasks [5,10,2,12], however, both consistency and complementarity are significant that it is a waste of information if we ignore one of them. In [11], they find a joint latent representation which include both common and special features of multiple views, and followed this work, [16] made some improvements.…”

Section: Introductionmentioning

confidence: 99%

Auto-encoder Based Co-training Multi-view Representation Learning

Lu¹,

Liu²,

Wang³

et al. 2019

Advances in Knowledge Discovery and Data Mining

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Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space learning. As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views. The algorithm has two stages, the first is to train auto-encoder of each view, and the second stage is to train a supervised network. Interestingly, the two stages share the weights partly and assist each other by co-training process. According to the experimental result, we can learn a well performed latent feature representation, and auto-encoder of each view has more powerful reconstruction ability than traditional auto-encoder.

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

Cited by 28 publications

References 48 publications

Multiview spectral clustering via complementary information

Multiview spectral clustering via complementary information

Multi-view Data Visualisation via Manifold Learning

Auto-encoder Based Co-training Multi-view Representation Learning

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