Relative margin induced support vector ordinal regression

Chen, Xingchi; Chen, Shuo; Zheng, Wei; Ye, Weidu

doi:10.1016/j.eswa.2023.120766

Cited by 22 publications

(3 citation statements)

References 40 publications

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“…65 Different from classification, the expression intensity has strictly ordinal relation which is an ordinal regression problem. 66,67…”

Section: Discussionmentioning

confidence: 99%

A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognition

Xu,

Du,

Wang

et al. 2023

Computational Intelligence

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Emotional recognition in conversations (ERC) is increasingly being applied in various IoT devices. Deep learning‐based multimodal ERC has achieved great success by leveraging diverse and complementary modalities. Although most existing methods try to adopt attention mechanisms to fuse different information, these methods ignore the complementarity between modalities. To this end, the joint cross‐attention model is introduced to alleviate this issue. However, multi‐scale feature information on different modalities is not utilized. Moreover, the context relationship plays an important role in feature extraction in the expression recognition task. In this paper, we propose a novel joint hierarchical graph convolution network (JHGCN) which exploits different layer features and context relationships for facial expression recognition based on audio‐visual (A‐V) information. Specifically, we adopt different deep networks to extract features from different modalities individually. For V modality, we construct V graph data based on patch embeddings which are extracted from the transformer encoder. Moreover, we embed the graph convolution which can leverage the intra‐modality relationships with the transformer encoder. Then, the deep feature from different layers is fed to the hierarchical fusion module to enhance feature representation. At last, we use the joint cross‐attention mechanism to exploit the complementary inter‐modality relationships. To validate the proposed model, we have conducted various experiments on the AffWild2 and CMU‐MOSI datasets. All results confirm that our proposed model achieves highly promising performance compared to the joint cross‐attention model and other methods.

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“…65 Different from classification, the expression intensity has strictly ordinal relation which is an ordinal regression problem. 66,67…”

Section: Discussionmentioning

confidence: 99%

A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognition

Xu,

Du,

Wang

et al. 2023

Computational Intelligence

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“…Furthermore, tropical regions are characterized by severe landscape fragmentation [25,76], and many rubber plantations are managed by smallholders with limited land area that is not easy to detect, which exacerbates the problems of collecting stand age and canopy height at the spatial resolution level of satellite imagery. Hence, a more advanced nonlinear regression fitting analyzer [77] that can synthetically consider all disturbance factors related to the rubber tree biomass assessment could be developed to raise the accuracy of the final results.…”

Section: Uncertainty Analysis and Potential Application Prospectsmentioning

confidence: 99%

Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China

Wang

Gao

et al. 2023

Remote Sensing

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Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink.

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“…Especially in the field of photovoltaic power generation forecasting, ML‐based technologies for PV power prediction get better results than existing physical methods 10 . For example, Support Vector Machine (SVM) 11–14 is applied on PV power prediction with good nonlinear mapping ability, which can accurately fit future PV power generation prediction. Similarly, nonlinear auto‐regression and exogenous vector input neural network (NARX) is proposed, which takes various meteorological factors as exogenous input to improve prediction performance 15 .…”

Section: Introductionmentioning

confidence: 99%

Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

Lu,

Chen,

et al. 2023

Computational Intelligence

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Photovoltaic (PV) power generation is widely utilized to satisfy the increasing energy demand due to its cleanness and inexhaustibility. Accurate PV power forecasting can improve the penetration of PV power in the grid. However, it is pretty challenging to predict PV power in short‐term under precious future meteorological information absence conditions. To address this problem, this study proposes the hybrid Contrastive Learning and Temporal Convolutional Network (CL‐TCN), and this forecasting approach consists of two parts, including model training and adaptive processes of forecasting models. In the model training stage, this forecasting method firstly trains 18 TCN models for 18 time points from 9:00 a.m. to 17:30 p.m. These TCN models are trained by only using historical PV power data samples, and each model is used to predict the next half‐hour power output. The adaptive process of models means that, in a practical forecasting stage, PV power samples from historical data are firstly evaluated and scored by a CL based data scoring mechanism to search for the most similar data samples to current measured samples. Then these similar samples are further applied to training a single above‐mentioned well‐trained TCN model to improve its performance in forecasting the next half‐hour PV power. The experimental results tested at the time resolution of 30 min demonstrate that the proposed approach has superior performance in forecasting accuracy not only in smooth PV power samples but also in fluctuating PV power samples. Moreover, the proposed CL based data scoring mechanism can filter useless data samples effectively accelerating the forecasting process.

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Relative margin induced support vector ordinal regression

Cited by 22 publications

References 40 publications

A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognition

A joint hierarchical cross‐attention graph convolutional network for multi‐modal facial expression recognition

Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China

Short‐term photovoltaic power forecasting using hybrid contrastive learning and temporal convolutional network under future meteorological information absence

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