Joint auto-weighted graph fusion and scalable semi-supervised learning

Bahrami, Saeedeh; Dornaika, Fadi; Bosaghzadeh, Alireza

doi:10.1016/j.inffus.2020.09.007

Cited by 27 publications

(20 citation statements)

References 39 publications

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“…From Table 2, our strategy achieves the maximum division precision possible. The musicality division has 94 percent accuracy in terms of speed, which is 19 percent greater than the speed limit division used in work [26][27][28][29]. Furthermore, in the context of kinematic highlights and beat, the impacts of division approaches are superior to those in the context of kinematic highlights.…”

Section: Recall = T P T P + E N Precision = T P T P + F N mentioning

confidence: 94%

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

Fang

2022

Wireless Communications and Mobile Computing

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The music development matching model and the quantifiable planning model have an undesirable fit between the dance produced by the model and the music self in terms of music-driven dance development age (e.g., generated dance development is lacking, and long-distance dance arrangements are lacking in perfection and discernment). The traditional methodology cannot produce new dance moves or other associated concerns. To address these concerns, we are working on a dance age estimation based on technological developments and neural networks that will eliminate the need for voice and development planning. The first stage uses the prosody elements and sound beat highlights extracted from music as music highlights, while the second stage uses the directions of essential human body issues derived from dance recordings as movement highlights. The model’s generating module acknowledges the vital planning of music and dance advancements to build a smooth dance posture in the next stage; the discriminator module acknowledges the autoencoder module agent has improved sound characteristics and the consistency of dance and music. In the third and final step, the model’s transformed form changes the dance act succession into a good diversity of dance. Finally, a reasonable rendition of the dance that matches the music has been found (e.g., trial data is gathered from online dance recordings, and the exploratory outcomes are examined from five perspectives: poor work esteem, correlation of various baselines, assessment of grouping age influence, client examination, and genuine dance recording quality assessment). The proposed dance age model has a reasonable impact on converting into actual dance recordings, according to the results.

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Section: Recall = T P T P + E N Precision = T P T P + F N mentioning

confidence: 94%

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

Fang

2022

Wireless Communications and Mobile Computing

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“…Beyond trustworthy decision, our approach automatically alleviates the impact from heavily noisy or corrupted modality. Compared with the intermediate fusion [4,43,44], our method can effectively distinguish which modality is noisy or corrupted for different samples, and accordingly can take the modality-specific uncertainty into account for robust integration.…”

Section: Multimodal Learningmentioning

confidence: 99%

Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions

Ma¹,

Han²,

Zhang³

et al. 2021

Preprint

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Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods mainly focus on improving the performance and often ignore the confidence of prediction for diverse situations. In this study, we are devoted to trustworthy multimodal regression which is critical in cost-sensitive domains. To this end, we introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Our model can be dynamically aware of uncertainty for each modality, and also robust for corrupted modalities. Furthermore, the proposed MoNIG ensures explicitly representation of (modality-specific/global) epistemic and aleatoric uncertainties, respectively. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks (e.g., temperature prediction for superconductivity, relative location prediction for CT slices, and multimodal sentiment analysis 3 ).

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“…To build a more robust adaptive neighborhood graph, Bo et al [13] proposed to learn latent representations in multi-view data and incorporate label information into the model using label propagation techniques. Bahrami et al [14] proposed a multiview learning method for graph fusion and label propagation that predicts the labels of unlabeled data while fusing graphs. However, there are two major limitations to such these methods that must be overcome [15]: on the one hand, as the key to the success of these methods is to construct appropriate similarity graphs and propagate the label information over the graph, different similarity measure functions or hyper-parameters will drastically alter the performance of the final task.…”

Section: Introductionmentioning

confidence: 99%

Multi-view Data Classification with a Label-driven Auto-weighted Strategy

Yuyuan¹,

Zhou²,

Huang³

et al. 2022

Preprint

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Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor performance. In this paper, by establishing a link between labeled data and the importance of different views, we propose an auto-weighted strategy to evaluate the importance of views from a label perspective to avoid the negative impact of unimportant or low-quality views. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model. The initialization of the proposed model can be effectively determined by labeled data, which is practical. The model is decoupled into three small-scale sub-problems that can efficiently be optimized with a local convergence guarantee. The experimental results on classification tasks show that the proposed method achieves optimal or sub-optimal classification accuracy at the lowest computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies on multi-view datasets with low-quality views.

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Joint auto-weighted graph fusion and scalable semi-supervised learning

Cited by 27 publications

References 39 publications

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions

Multi-view Data Classification with a Label-driven Auto-weighted Strategy

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