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Matrix factorization with a sigmoid-like loss control

NEUROCOMPUTING 2024年 576卷

作者： Xu, Yuan -Yuan Xiao, Hui Zhang, Heng-Ru Wu, Wei-Zhi Min, Fan Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Southwest Petr Univ Lab Machine Learning Chengdu 610500 Peoples R China Zhejiang Ocean Univ Sch Informat Engn Zhoushan 316022 Peoples R China Zhejiang Ocean Univ Key Lab Oceanog Big Data Min & Applicat Zhejiang P Zhoushan 316022 Peoples R China

Matrix factorization is one of the fundamental approaches of recommender systems. With the popular L2 loss, learning models tend to overfit significantly deviated predictions. However, predicting the actual rating of 5 as 1 or 2 makes no essential difference in the application. In this paper, we design a sigmoid-like function to control the loss of each individual prediction, which has two advantages. First, it reduces the loss corresponding to significantly deviated predictions. Therefore, the impact of these predictions, some of which may be caused by outliers, is also reduced. Second, it is independent of two classical over -fitting control techniques using regular terms and validation data, respectively. Hence, it can be combined with them to form a more powerful method. Experiments are undertaken on six benchmark datasets in comparison with different losses. Results show that the proposed loss function has good performance in terms of MAE, RMSE, and NDCG, however not so good in terms of HR and MAP.

关键词： Loss function Matrix factorization Over-fitting Recommender system

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NC-ALG: Graph-Based Active learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks labeling Noise measurement

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A Top -K QoS-Optimal Service Composition Approach Based on Service Dependency Graph

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JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2021年第3期33卷 50-68页

作者： Zhang, Baili Wen, Kejie Lu, Jianhua Zhong, Mingjun Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Supreme Court China Res Ctr Judicial Big Data Nanjing Jiangsu Peoples R China Minist Educ Key Lab Comp Network & Informat Integrat Nanjing Jiangsu Peoples R China Univ Lincoln Sch Comp Sci Machine Learning Lincoln Lincs England

With the development of internet of things (IoT) technology, servitization of IoT device functions has become a trend. The cooperation between IoT devices can be equivalent to web service composition. However, current service composition approaches applied in the internet cannot work well in IoT environments due to weak adaptability, low accuracy, and poor time performance. This paper, based on service dependency graph, proposes a top -k QoS-optimal service composition approach suitable for IoT. It aims to construct the relationship between services by applying the service dependency model and to reduce the traversal space through effective filtering strategies. On the basis of a composition path traversal sequence, the generated service composition can be represented directly to avoid backtracking search. Meanwhile, the redundant services can be removed from the service composition with the help of dynamic programming. Experiments show that the approach can obtain the top -k QoS-optimal service composition and better time performance.

关键词： Dependency Graph IoT Path Traversal Sequence QoS Service Composition Top-K

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A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer ? s disease classification

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MAGNETIC RESONANCE IMAGING 2021年 78卷 119-126页

作者： Zhang, Jie Zheng, Bowen Gao, Ang Feng, Xin Liang, Dong Long, Xiaojing Chinese Acad Sci Shenzhen Inst Adv Technol Reasearch Ctr Med AI Shenzhen Guangdong Peoples R China Chongqing Univ Technol Comp Sci & Engn Chongqing Peoples R China Big Data & Machine Learning Lab Chongqing Peoples R China Key Lab Magnet Resonance & Multimodal Imaging Gua Guangzhou Guangdong Peoples R China

Purpose: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or prediction on the conversion from mild cognitive impairment (MCI) to AD. In this study, we aimed to develop a new deep learning method to detect or predict AD in an efficient way. Materials and methods: We proposed a densely connected convolution neural network with connection-wise attention mechanism to learn the multi-level features of brain MR images for AD classification. We used the densely connected neural network to extract multi-scale features from pre-processed images, and connection wise attention mechanism was applied to combine connections among features from different layers to hierarchically transform the MR images into more compact high-level features. Furthermore, we extended the convolution operation to 3D to capture the spatial information of MRI. The features extracted from each 3D convolution layer were integrated with features from all preceding layers with different attention, and were finally used for classification. Our method was evaluated on the baseline MRI of 968 subjects from ADNI database to discriminate (1) AD versus healthy subjects, (2) MCI converters versus healthy subjects, and (3) MCI converters versus non-converters. Results: The proposed method achieved 97.35% accuracy for distinguishing AD patients from healthy control, 87.82% for MCI converters against healthy control, and 78.79% for MCI converters against non-converters. Compared with some neural networks and methods reported in recent studies, the classification performance of our proposed algorithm was among the top ranks and improved in discriminating MCI subjects who were in high risks of conversion to AD. Conclusions: Deep learning techniques provide a powerful tool to explore minute but intricate characteristics in MR image

关键词： Convolutional neural network Attention mechanism Early detection Structural MRI

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Relation-Aggregated Cross-Graph Correlation learning for Fine-Grained Image-Text Retrieval

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第2期35卷 2194-2207页

作者： Peng, Shu-Juan He, Yi Liu, Xin Cheung, Yiu-ming Xu, Xing Cui, Zhen Huaqiao Univ Dept Artificial Intelligence Xiamen 361021 Peoples R China Key Lab Pattern Recognit & Comp Vis Xiamen 361021 Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Comp Vis & Machine Learning Xiamen 361021 Peoples R China Fujian Key Lab Big Data Intelligence & Secur Xiamen 361021 Peoples R China Huaqiao Univ Dept Comp Sci Xiamen 361021 Peoples R China Zhejiang Lab Artificial Intelligence Res Inst Hangzhou 311121 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Univ Elect Sci & Technol China Ctr Future Multimedia Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Fine-grained image-text retrieval has been a hot research topic to bridge the vision and languages, and its main challenge is how to learn the semantic correspondence across different modalities. The existing methods mainly focus on learning the global semantic correspondence or intramodal relation correspondence in separate data representations, but which rarely consider the intermodal relation that interactively provide complementary hints for fine-grained semantic correlation learning. To address this issue, we propose a relation-aggregated cross-graph (RACG) model to explicitly learn the fine-grained semantic correspondence by aggregating both intramodal and intermodal relations, which can be well utilized to guide the feature correspondence learning process. More specifically, we first build semantic-embedded graph to explore both fine-grained objects and their relations of different media types, which aim not only to characterize the object appearance in each modality, but also to capture the intrinsic relation information to differentiate intramodal discrepancies. Then, a cross-graph relation encoder is newly designed to explore the intermodal relation across different modalities, which can mutually boost the cross-modal correlations to learn more precise intermodal dependencies. Besides, the feature reconstruction module and multihead similarity alignment are efficiently leveraged to optimize the node-level semantic correspondence, whereby the relation-aggregated cross-modal embeddings between image and text are discriminatively obtained to benefit various image-text retrieval tasks with high retrieval performance. Extensive experiments evaluated on benchmark datasets quantitatively and qualitatively verify the advantages of the proposed framework for fine-grained image-text retrieval and show its competitive performance with the state of the arts.

关键词： Semantics Correlation Visualization Electronic mail Task analysis Media Feature extraction Cross-graph relation encoder fine-grained correspondence image-text retrieval intermodal relation

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CNN-BI-LSTM-CYP: A deep learning approach for sugarcane yield prediction

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 57卷

作者： Saini, Preeti Nagpal, Bharti Garg, Puneet Kumar, Sachin Guru Gobind Singh Indraprastha Univ GGSIPU USICT NSUT East Campus Delhi India NSUT East Campus Dept Comp Sci & Engn Delhi India ABES Engn Coll Dept Comp Sci & Engn Ghaziabad UP India South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk 454080 Russia

Sugarcane (Saccharum officinarum L.) is one of the principal origins of sugar and is also known as the main cash crop of India. About 19.07% of the total production of the world's sugar requirement is fulfilled by India. Traditionally, Statistical approaches have been utilized for Crop yield prediction, which is tedious and timeconsuming. In this direction, the present work proposed a novel hybrid CNN-Bi-LSTM_CYP deep learningbased approach that includes convolutional layers to extract the relevant spatial information in a sequence to Bi-LSTM layers that recognize the Phenological long-term and short-term bidirectional dependencies in the dataset to predict the Sugarcane crop yield. The experimentation was performed and validated on the historical dataset from 1950 to 2019 years of the major Sugarcane-producing states of India. The preliminary results shown that the CNN-Bi-LSTM_CYP method performed well (RMSE:4.05, MSE:16.40) in comparison to traditional Stacked-LSTM (RMSE:8.8, MSE:77.79), ARIMA (RMSE:5.9, MSE:34.80), GPR (RMSE:10.1, MSE:103.3), and Holtwinter Time-series (RMSE:9.9, MSE:99.7) techniques. The study concluded that the predicted sugar yield has a minimal relative error concerning the ground truth data for the CNN-Bi-LSTM_CYP approach proving the proposed model's efficiency.

关键词： Forecasting Yield Prediction ML-GPR CNN-Bi-LSTM ARIMA GRU

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A study on the uncertainty of convolutional layers in deep neural networks

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INTERNATIONAL JOURNAL OF machine learning AND CYBERNETICS 2021年第6期12卷 1853-1865页

作者： Shen, Haojing Chen, Sihong Wang, Ran Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen 518060 Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen 518060 Peoples R China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.

关键词： Uncertainty Adversarial training Convolution LeNet Min– Max property

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TEEM: Two-factor Energy Evaluation Metric toward Green big data System

TEEM: Two-factor Energy Evaluation Metric toward Green Big D...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Fang, Weidong Zhu, Chunsheng Guizani, Mohsen Li, Zhiqi Zhang, Wuxiong Rodrigues, Joel J. P. C. Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Sci & Technol Microsyst Lab Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Res & Dev Ctr Micro Nano Elect Shanghai Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates Lusofona Univ COPELABS Lisbon Portugal

ISBN: (纸本)9798350310900

Toward green big data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

关键词： green big data system energy efficiency evaluation metrics transmission distance modulation level

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Fisher-regularized Support Vector machine with Pinball Loss Function

Fisher-regularized Support Vector Machine with Pinball Loss ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Zhengqi Zhang, Li Zhang, Zhao Soochow Univ Sch Comp Sci & Technol Joint Int Res Lab Machine Learning & Neuromorph C Suzhou Peoples R China Soochow Univ Sch Comp Sci & Technol Joint Int Res Lab Machine Learning & Neuromorph C Prov Key Lab Comp Informat Proc Technol Suzhou Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Key Lab Knowledge Engn Big Data Minist Educ Hefei Peoples R China

ISBN: (纸本)9780738133669

Fisher-regularized support vector machine (FisherSVM) can approximatively fulfill the Fisher criterion and obtain good statistical separability, which is a combined method of the support vector machine and Fisher discriminant analysis. However, the hinge loss function is related to the shortest distance between two-class sets, and FisherSVM may be hence sensitive to noise. To remedy it, the pinball loss function, which is related to the quantile distance, is introduced into FisherSVM and then a Fisher-regularized support vector machine with pinball loss function (Pin-FisherSVM) is proposed, which combines the noise insensitivity of the pinball loss function with the statistical separability of FisherSVM well. Pin-FisherSVM can be cast as a quadratic programming, which results a globally optimal solution. Experimental results on artificial and real-world datasets demonstrate that our proposed method is insensitive to label noise or feature noise. Compared to FisherSVM, the proposed Pin-FisherSVM has the same computational complexity and exhibits superior noise insensitivity.

关键词： Pinball loss function Fisher regularization Support vector machine Noise insensitivity

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Spatial and temporal consistency learning for monocular 6D pose estimation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 131卷

作者： Zhang, Hong-Bo Liang, Jia-Yu Hong, Jia-Xin Lei, Qing Liu, Jing-Hua Du, Ji-Xiang Huaqiao Univ Sch Comp Sci & Technol Xiamen 361021 Fujian Peoples R China Natl Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361021 Fujian Peoples R China Huaqiao Univ Fujian Prov Univ Key Lab Comp Vis & Machine Learning Xiamen 361021 Fujian Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361021 Fujian Peoples R China

Monocular 6D pose estimation is a challenging task in the field of computer vision and robotics. Many previous works only input the cropped image of single object during training and inference, aiming to remove the noise from non-object regions. However, most of these methods ignore the viewpoint and spatial relationships of objects in the scene, which are crucial for accurate pose estimation of camera. To address this issue, this paper proposes a novel multi-view and multi-object based learning strategy for monocular 6D pose estimation, which involves the consistency of object coordinate for the same object at different viewpoints and the consistency of world coordinate for different objects in the same space. In the proposed method, the spatial and temporal groups are generated to trained the monocular 6D pose estimation network. Due to the camera motion, scene images taken at different times can be regarded as images captured from different viewpoints. Therefore, a temporal consistency loss is designed to constraint the relationship of the same object at different viewpoints, while a spatial consistency loss is designed to constraint the relationship of different objects at the same space. Finally, the proposed method is verified on the public datasets. Experimental results show that the proposed method is accurate, robust, and outperforms similar state-of-the-art approaches.

关键词： Monocular 6D pose estimation Spatial consistency Temporal consistency Spatial and temporal groups

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