Representation Learning in Multi-view Clustering: A Literature Review

Chen, Man-Sheng; Lin, Jiaqi; Li, Xianglong; Liu, Baoyu; Wang, Chang‐Dong; Huang, Dong; Lai, Jianhuang

doi:10.1007/s41019-022-00190-8

Cited by 40 publications

(10 citation statements)

References 161 publications

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“…SC tidak mampu memahami permasalahan yang diberikan. Sejalan dengan (Chen et al, 2022) hanya sebagian informasi yang bisa ditangkap dari pandangan individu. Dari jawaban SC tersebut dapat dipastikan bahwa SC tidak memahami permasalahanan yang diberikan.…”

Section: Kemampuan Siswa Pada Indikator Indeksunclassified

Representasi Semiotik Matematis Siswa SMA Dalam Masalah Aplikasi Turunan Fungsi Aljabar

Dewi

Hakim²

2023

educatio

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Kemampuan dalam matematika diantaranya adalah interpretasi sebuah tanda. Kurangnya kemampuan siswa dalam menginterpretasi tanda didalam matematika menyebabkan siswa tidak mampu dalam menyelesaikan masalah aplikasi turunan fungsi aljabar. Kemampuan representasi semiotik maematis didasarkan pada konsep Peirce untuk memperlihatkan kemampuan siswa dalam mengungkapkan tanda dari suatu fenomena atau lambang yang mewakilinya merujuk pada suatu objek. Oleh karena itu, kajian ini bertujuan untuk mendeksripsikan tentang kemampuan representasi semiotik matematis dengan subjek yang digunakan siswa dari kelas XII disalah satu SMA di Kabupaten Karawang, sebanyak 3 subjek terpilih berdasarkan pencapaian siswa pada setiap indikator kemampuan representasi semiotik matematis. Kemampuan representasi semiotik matematis diukur berdasarkan 1) mampu menyajikan data atau informasi kedalam tampilan gambar yang dapat dilihat secara visual; 2) mampu menyatakan masalah, menggunakan simbol dan menginterpretasikan simbol ke dalam matematika; 3) mampu menyimpulkan suatu tampilan representasi dengan menggunakan kata-kata. Berdasarkan hasil analisis jawaban, diperoleh informasi bahwa SA mampu 1) menyajikan data atau informasi kedalam tampilan gambar yang dapat dilihat secara visual; 2) mampu menyatakan masalah, menggunakan simbol dan menginterpretasikan simbol ke dalam matematika; 3) mampu menyimpulkan suatu tampilan representasi dengan menggunakan kata-kata. Sedangkan SB hanya mampu 1) menyatakan masalah, menggunakan simbol dan menginterpretasikan simbol ke dalam matematika; dan 2) mampu menyimpulkan suatu tampilan representasi dengan menggunakan kata-kata. Sedangkan SC tidak mampu memenuhi salah satunya.

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Section: Kemampuan Siswa Pada Indikator Indeksunclassified

Representasi Semiotik Matematis Siswa SMA Dalam Masalah Aplikasi Turunan Fungsi Aljabar

Dewi

Hakim²

2023

educatio

View full text Add to dashboard Cite

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“…Unlike the previous review, a binary-code-learning-based multi-view clustering algorithm was mentioned. Chen et al (2022a) divided multi-view clustering into representation-based learning and non-representation-based learning according to the perspective of representation learning. Among them, the graph-based multi-view clustering and the subspace-based multi-view clustering were partitioned into shallow representation learning, and the deep-learning-based multi-view clustering method was partitioned into deep-representation learning.…”

Section: Introductionmentioning

confidence: 99%

“…Song et al (2022) outlined the large-scale graph-based semi-supervised classification algorithms in terms of graph construction, graph regularization methods, and graph embedding methods, respectively. As can be seen, Xu et al (2013), Yang and Wang (2018), Zhang et al (2019), Wang et al (2019a), Fu et al (2020) and Chen et al (2022a) mainly summarized multi-view clustering according to different principles, such as graphbased, subspace-based, multi-kernel-based, etc. However, a summary of large-scale multiview clustering methods is lacking.…”

Section: Introductionmentioning

confidence: 99%

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

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The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can better use different views’ consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.

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“…C LUSTERING is one of the most fundamental tasks of unsupervised learning [1], [2], [3], [4], [5], [6], [7]. The basic idea is to divide the data into several disjoint clusters, where the samples in the same cluster are highly similar to each other, and the samples in different clusters are less related.…”

Section: Introductionmentioning

confidence: 99%

“…https://github.com/HKUST-KnowComp/MNE 6. https://github.com/tkipf/gae 7. https://github.com/GRAND-Lab/ARGA…”

mentioning

confidence: 99%

Multi-view Attributed Graph Clustering

Lin

Kang

Zhang

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Multi-view attributed graph clustering is an important approach to partition multi-view data based on the attribute feature and adjacent matrices from different views. Some attempts have been made in utilizing Graph Neural Network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel Dual Information enhanced multi-view Attributed Graph Clustering (DIAGC) method is proposed in this paper. Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations. Besides, the Mutual Information Maximization (MIM) module maximizes the agreement between the latent high-level representation and lowlevel ones, and enables the high-level representation to satisfy the desired clustering structure with the help of the Self-supervised Clustering (SC) module. Extensive experiments on several realworld benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

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Representation Learning in Multi-view Clustering: A Literature Review

Cited by 40 publications

References 161 publications

Representasi Semiotik Matematis Siswa SMA Dalam Masalah Aplikasi Turunan Fungsi Aljabar

Representasi Semiotik Matematis Siswa SMA Dalam Masalah Aplikasi Turunan Fungsi Aljabar

The methods for improving large-scale multi-view clustering efficiency: a survey

Multi-view Attributed Graph Clustering

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