Graph regularized Lp smooth non-negative matrix factorization for data representation

Leng, Chengcai; Hai, Zhang; Cai, Guorong; Cheng, Irene; Basu, Anup

doi:10.1109/jas.2019.1911417

Cited by 69 publications

(22 citation statements)

References 32 publications

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“…And the third one is that some extracted causal pathways contain the implicit mediators (or the implicit effect/causative-concept EDUs) as the implicit wrdCoc features which have to be represented by the explicit wedCoc features for clear comprehensible pathways. Moreover, our implicit wrdCoc features are the qualitative data whereas the previous research [16] discovered the hidden semantics or the latent semantics as the implicit features by the graph regularization where the latent semantics of [16] is the quantitative data.…”

Section: Edu1 "เมืmentioning

confidence: 94%

Causal Pathway Extraction from Web-Board Documents

Pechsiri

Piriyakul

2021

Applied Sciences

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This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents.

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Section: Edu1 "เมืmentioning

confidence: 94%

Causal Pathway Extraction from Web-Board Documents

Pechsiri

Piriyakul

2021

Applied Sciences

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“…An LF model is widely adopted to implement an RS [17,18]. So far, various sophisticated LF models have been proposed, including a bias-based one [18], a nonparametric one [31], a non-negativity-constrained one [19], a probabilistic one [30], a dual-regularization-based one [33], a posterior-neighborhood-regularized one [42], a randomized one [43], a graph regularized one [44], a neighborhood-and-location integrated one [32], a confidence-driven one [34], and a data characteristic-aware one [35]. Although they are different from each other in objective functions or learning algorithms, they all adopt an L 2 norm-oriented Loss that is highly sensitive to outliers [25,27,28].…”

Section: B Related Workmentioning

confidence: 99%

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

Shang

Luo

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

102

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A recommender system is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a recommender system. However, current LF models mostly adopt single distance-oriented Loss like an L 2 norm-oriented one, which ignores target data's characteristics described by other metrics like an L 1 norm-oriented one. To investigate this issue, this paper proposes an L 1 -and-L 2 -norm-oriented Latent Factor (L 3 F) model. It adopts two-fold ideas: a) aggregating L 1 norm's robustness and L 2 norm's stability to form its Loss, and b) adaptively adjusting weights of L 1 and L 2 norms in its Loss. By doing so, it achieves fine aggregation effects with L 1 norm-oriented Loss's robustness and L 2 norm-oriented Loss's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an L 3 F model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

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“…Tensors can be expressed in either a fibrous or a slice form [24,25]. Any two dimensions in the third-order tensor are kept unchanged.…”

Section: Tensor Theorymentioning

confidence: 99%

Expressway Traffic Flow Missing Data Repair Method Based on Coupled Matrix-Tensor Factorizations

Jiang

Deng

2021

Mathematical Problems in Engineering

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Traffic flow data is the basis of traffic management, planning, control, and other forms of implementation. Once missing, it will directly affect the monitoring and prediction of expressway traffic status. Regarding this, this paper proposes a repair method for the traffic flow missing data of expressway, combined with the idea of coupled matrix-tensor factorizations (CMTF), to couple the auxiliary traffic flow data into the main traffic flow data and to construct the coupling matrix-tensor expression of traffic flow data, and the alternating direction multiplier algorithm is used to realize the repair of missing traffic flow data. Combined with the measured data of expressway traffic flow, the experimental results show that, under different missing data types and missing rates, the proposed method outperforms the methods lacking auxiliary traffic flow data and achieves a good repair effect, especially for high miss data rates.

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Graph regularized Lp smooth non-negative matrix factorization for data representation

Cited by 69 publications

References 32 publications

Causal Pathway Extraction from Web-Board Documents

Causal Pathway Extraction from Web-Board Documents

An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems

Expressway Traffic Flow Missing Data Repair Method Based on Coupled Matrix-Tensor Factorizations

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Graph regularized Lp smooth non-negative matrix factorization for data representation

Cited by 69 publications

References 32 publications

Causal Pathway Extraction from Web-Board Documents

Causal Pathway Extraction from Web-Board Documents

An L 1-and-L 2-Norm-Oriented Latent Factor Model for Recommender Systems

Expressway Traffic Flow Missing Data Repair Method Based on Coupled Matrix-Tensor Factorizations

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Product

Resources

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An L ₁-and-L ₂-Norm-Oriented Latent Factor Model for Recommender Systems