Self-representation and matrix factorization based multi-view clustering

Dou, Yong; Yun, Yu; Gao, Quanxue; Zhang, Xiangdong

doi:10.1016/j.neucom.2021.06.092

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…In addition, it is a clever way to reduce the computational complexity of LSMVC algorithms by rotating the constructed third-order tensor. Therefore, combined with third-order tensor decomposition, many multi-view clustering algorithms have been proposed in recent years (Li et al 2021;Wu et al 2019Wu et al , 2020Jiang and Gao 2022;Chen et al 2019Chen et al , 2021Chen et al , 2022cZhang et al 2021;Xu et al 2020;Fu et al 2022;Yang et al 2022c;Xia et al 2021Xia et al , 2022aSun et al 2020;Tang et al 2021;Ma et al 2022;Dou et al 2021;Zheng et al 2020Zheng et al , 2023Hao et al 2022;Gao et al 2020). This section first introduces the third-order t-SVD, then the reduction of computational complexity caused by the third-order rotation will be analyzed theoretically.…”

Section: Third-order Tensor T-svd Based Lsmvcmentioning

confidence: 99%

“…P ∈ R N×N×V is the third-order transition probability tensor. P ∈ R N×V×N is the rotation of P 2022; Yang et al 2022c;Xia et al 2021Xia et al , 2022aSun et al 2020;Tang et al 2021;Ma et al 2022;Dou et al 2021;Zheng et al 2020Zheng et al , 2023Hao et al 2022;Gao et al 2020). This paper classifies them into three categories according to the time complexity.…”

Section: Algorithms Based On Third-order Tensor T-svdmentioning

confidence: 99%

“…Generally, since the power of the number of samples N directly determines the time complexity of the algorithm, only the power of N is considered here. The three categories are O(N 3 ) (Xie et al 2018(Xie et al , 2021(Xie et al , 2022aLi et al 2021;Wu et al 2019Wu et al , 2020Chen et al 2019Chen et al , 2021Zhang et al 2021;Sun et al 2020;Ma et al 2022;Dou et al 2021;Zheng et al 2020Zheng et al , 2023Hao et al 2022;Gao et al 2020), O(N 2 ) (Xu et al 2020;Fu et al 2022;Chen et al 2022c), O(N) (Jiang and Gao 2022;Yang et al 2022c;Tang et al 2021;Xia et al 2022b), respectively. Each category will be introduced separately in the following.…”

Section: Algorithms Based On Third-order Tensor T-svdmentioning

confidence: 99%

“…Xie et al (2018), Zhang et al (2021), Chen et al (2019), Xia et al (2021), Sun et al (2020), Dou et al (2021), Zheng et al (2020Zheng et al ( , 2023, Hao et al (2022) and Gao et al (2020) learned the representation matrices of each view from the original multi-view data based on the subspace representation and constructs them as a third-order tensor to learn the high-dimensional correlation. Among them, the time complexity of Xie et al (2018Xie et al ( , 2021, Zhang et al (2021), Chen et al (2019), Sun et al (2020), Dou et al (2021) and Zheng et al (2020) is O(N 3 ) due to the spectral clustering of the similarity matrix obtained after optimization. In addition, Zhang et al (2021) added SMR (SMooth Representation) (Hu et al 2014) to maintain the local geometric structure.…”

Section: Algorithms Based On Third-order Tensor T-svdmentioning

confidence: 99%

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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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Section: Third-order Tensor T-svd Based Lsmvcmentioning

confidence: 99%

Section: Algorithms Based On Third-order Tensor T-svdmentioning

confidence: 99%

Section: Algorithms Based On Third-order Tensor T-svdmentioning

confidence: 99%

Section: Algorithms Based On Third-order Tensor T-svdmentioning

confidence: 99%

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

Yang,

Tan

2024

Artif Intell Rev

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“…The method of the recommendation system has brought a new perspective to solve the problem of student grades prediction. In particular, the matrix decomposition method decomposes the student curriculum score matrix into two low-rank matrices to represent the curriculum and student potential knowledge space, and then obtains the scores of the unfinished courses through the dot product of the corresponding vectors in the two decomposition matrices [26]. The depth matrix decomposition model was successfully applied in the field of recommendation systems.…”

Section: Deep Matrix Factorizationmentioning

confidence: 99%

Study on Score Prediction Model with High Efficiency Based on Deep Learning

Yang

Bai

2022

Electronics

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In the problem of unified classroom performance prediction, there is a certain lag in the prediction, and there are also problems such as the data sparsity and single feature in the data. In addition, feature engineering is often carried out manually in modeling, which highly depends on the professional knowledge and experience of engineers and affects the accuracy of the prediction to a certain extent. To solve the abovementioned gaps, we proposed an online course score prediction model with a high time efficiency that combines multiple features. The model uses a deep neural network, which can automatically carry out feature engineering and reduce the intervention of artificial feature engineering, thus significantly improving the time efficiency. Secondly, the model uses a factorization machine and two kinds of neural networks to consider the influence of first-order features, second-order features, and higher-order features at the same time, and it fully learns the relationship between the features and scores, which improves the prediction effect of the model compared to using only single feature learning. The performance of the model is evaluated on the learning analysis dataset from Fall 2015 to Spring 2021 and includes 412 courses with 600 students. The experimental results show that the performance of the prediction model based on the feature combination proposed in the present study is better than the previous performance prediction model. More importantly, our model has the best time efficiency of below 0.3 compared to the other models.

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