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Unsupervised feature selection with evolutionary sparsity

NEURAL NETWORKS 2025年 189卷 107512页

作者： Zhou, Shixuan Xiang, Yi Huang, Han Huang, Pei Peng, Chaoda Yang, Xiaowei Song, Peng South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot SCUT Guangzhou 510006 Peoples R China Guangdong Engn Ctr Large Model & GenAI Technol Guangzhou 510006 Peoples R China Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510006 Peoples R China South China Agr Univ Sch Math & Informat Guangzhou 510006 Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

The t2,0-norm is playing an increasingly important role in unsupervised feature selection. However, existing algorithm for optimization problem with t2,0-norm constraint has two problems: First, they cannot automatically determine the sparsity, also known as the number of key features. Second, they have the risk of converging towards local optima, therefore selecting trivial (less informative) features. To address these problems, this paper proposes an unsupervised feature selection method with evolutionary sparsity (EVSP), which integrates the feature selection process with a sparse projection matrix and population search mechanisms into a unified unsupervised feature selection framework. Specifically, the level of sparsity is encoded as population individuals, and subsequently, a multi-objective evolutionary algorithm based on binary encoding is introduced to recursively determine the optimal level of sparsity, thus unsupervisedly guiding the learning of an optimal row-sparse projection matrix. Moreover, by utilizing the feature weights learned through sparse projection, a two-stage strategy called the mutation-repair operator is designed to steer the evolution of the population, aiming to generate high-quality candidate solutions. Comprehensive experiments on eleven benchmark datasets, with a maximum dimensionality of 10304 features and a maximum size of 9298 samples, demonstrate that the proposed EVSP method can effectively determine the optimal sparsity level, significantly outperforming several state-of-the-art methods.

关键词： Unsupervised feature selection Multi-objective evolution Sparse projection

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A novel ship detection method based on Gabor feature 5

A novel ship detection method based on Gabor feature

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IET International Radar Conference 2020, IET IRC 2020

作者： Chang, Jiahui Fu, Zewen Li, Ning Chang, Jike College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) Systems can be used for discovery and surveillance. According to the current requirements, accurate ship detection is becoming more and more important. However, the accuracy of traditional ship detection algorithm is not satisfying in complex scene. As a consequence, this paper proposes a novel method for detecting ships within SAR imagery using Gabor feature and Two Parameter Constant False Alarm Rate (2P-CFAR) detection technique. First, feature extraction was performed by using Gabor filter. Then, combined with the extracted orientation feature, the ship recognition of SAR image is obtained by 2P-CFAR detector. Finally, false alarms among the candidate targets were removed by geometric features of the ships. To prove the rationality of this method, experiments were carried with two high-resolution Gaofen-3 satellite SAR images in open sea background. The experimental results show that the presented method has preferable performance in the accuracy of ship identification. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Synthetic aperture radar

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A new vegetation index for cooperative inversion of winter wheat covered surface soil moisture based on sentinel-1/2 remote sensing data 5

A new vegetation index for cooperative inversion of winter w...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Bei Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Aiming at the problem of the interfere of winter wheat on radar backscattering coefficient in surface soil moisture inversion, a new vegetation index called Fusion Vegetation Index (FVI) is defined in this study. Based on the Multi-Spectral Imager (MSI) data of Sentinel-2, and the Synthetic Aperture Radar (SAR) data of Sentinel-1, using FVI to retrieve water content of winter wheat, combined with Water Cloud model, the interference of winter wheat in soil moisture inversion was reduced. The results show that good soil moisture retrieval results can be obtained by combining with FVI. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Soil moisture

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On scattering characteristics of winter wheat at different phenological period based on Sentinel-1A SAR images 5

On scattering characteristics of winter wheat at different p...

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IET International Radar Conference 2020, IET IRC 2020

作者： Xu, Dongya Zhao, Jianhui Li, Ning College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Phenophase period refers to the periodic natural phenomena of plants under the influence of environmental factors and human activities. In recent years, synthetic aperture radar (SAR) has been used to growth monitoring of crops. Due to complex morphological changes at different growth stage of crops, single-phase SAR data unable provide sufficient information to analyze scattering characteristics at different phenological period. In this paper, the scattering characteristics of winter wheat are studied, using time series dual-polarization Sentinel-1A SAR images. And winter wheat planting region in Kaifeng city Henan Province is selected as the study area. Time series C-band radar backscatter coefficient is extracted, then analyze correlation between numerical evolution and phenological change with different polarization data. The variation trend is verified by field measured data. The results show that dual-polarization SAR image is great practical significance for winter wheat phenological monitoring. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Time series

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Multimodal Representation with Embedded Visual Guiding Objects for Named Entity Recognition in Social Media Posts 20

Multimodal Representation with Embedded Visual Guiding Objec...

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28th ACM International Conference on Multimedia (MM)

作者： Wu, Zhiwei Zheng, Changmeng Cai, Yi Chen, Junying Leung, Ho-fung Li, Qing South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Sch Software Engn Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781450379885

Visual contexts often help to recognize named entities more precisely in short texts such as tweets or snapchat. For example, one can identify "Charlie" as a name of a dog according to the user posts. Previous works on multimodal named entity recognition ignore the corresponding relations of visual objects and entities. Visual objects are considered as fine-grained image representations. For a sentence with multiple entity types, objects of the relevant image can be utilized to capture different entity information. In this paper, we propose a neural network which combines object-level image information and character-level text information to predict entities. Vision and language are bridged by leveraging object labels as embeddings, and a dense co-attention mechanism is introduced for fine-grained interactions. Experimental results in Twitter dataset demonstrate that our method outperforms the state-of-the-art methods.

关键词： multimodal named entity recognition modality gap fine-grained image representations

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UEIN: A User Evolving Interests Network for Click-Through Rate Prediction

UEIN: A User Evolving Interests Network for Click-Through Ra...

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big data Science and Engineering (bigdataSE), IEEE International Conference on

作者： Jianxiong Xu Xiaoyu Shi Hezhe Qiao Mingsheng Shang Xianbo He Qingyu He The Computer School of China West Normal University Nanchong Sichuan China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China College of Land and Resources China West Normal University Nanchong Sichuan China

ISBN: (纸本)9781665400398

The Click-Through Rate (CTR) prediction via modeling the change of user interest is of great significance task to industrial applications. Most of the existing methods based on collaborative filtering (CF) and recurrent neural network (RNN) that focus on capturing user's long-term and short-term interests separately, or combine these interests in a tough way. However, the effect of long- and short-term interests accounts different ratios to the next-item decision of user, rather than equally. To address this issue, we propose UEIN, a novel User Evolving Interest Network for CTR prediction task, that can capture the change of user preference via adaptively fusion the user's long-term and short-term interests over time. To explicitly learn the user preference on item category, we add the item category information into user behavior sequence, by clustering the items into different categories. Then we divide user historical behaviors into two sequence parts, long-term and short-term behaviors sequence. Based on this, we leverage the attention network to learn the user's current interest from the short-term behaviors sequence, while a dense network is adopted to extract the user's general interest from the long-term sequence. After that, an adaptive fusion mechanism is proposed to combine the current and general interest adaptively, and the prediction result is generated based on the user's combined interest. Extensive experimental results on industrial datasets demonstrated that the superiority of our proposed model, compared to the state-of-the-art models.

关键词： Adaptation models Recurrent neural networks Conferences Collaborative filtering Clustering methods Predictive models Feature extraction

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IIoT Mobile Business data Placement Strategy Based on Bayesian Optimization Algorithm

IIoT Mobile Business Data Placement Strategy Based on Bayesi...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Zhijie Shen Bowen Liu Xiaolong Xu Lianyong Qi Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies.

关键词： Cloud computing Systematics Memory User experience Bayes methods Servers Task analysis

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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： Artificial intelligence breast cancer digital pathology whole slide images

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Heterogenous Multi-Source Fusion for Ship Trajectory Complement and Prediction with Sequence Modeling

Heterogenous Multi-Source Fusion for Ship Trajectory Complem...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Changmeng Zheng Qi Peng Xuemiao Xu Key Laboratory of Big Data and Intelligent Robot School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Effectively monitoring ships and discovering abnormal ship trajectory in time is necessary for marine traffic supervision. The basic work of discovering the ship's abnormal trajectory is to predict the ship's navigation dynamically. Previous works in ship trajectory prediction are basically concern on single-source data, for example, the AIS data. These methods ignore the relations between different sources which may improve the performance of predicting ship trajectory. We propose a neural sequence model based on heterogeneous multisource fusion for ship trajectory completion and prediction. Our method makes better utilization of AIS, GPS and ARPA radar information to predict ship trajectory precisely. We construct a dataset which contains about 8 million ship trajectory samples and the experiments demonstrate that our multi-source fusion model gains promising results.

关键词：

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Graph Edit Distance reward: Learning to edit scene graph

arXiv

引用

arXiv 2020年

作者： Chen, Lichang Lin, Guosheng Wang, Shijie Wu, Qingyao Nanyang Technological University Zhejiang University Huazhong University of Science and Technology School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education

Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA. In this paper, we propose a new method to edit the scene graph according to the user instructions, which has never been explored. To be specific, in order to learn editing scene graphs as the semantics given by texts, we propose a Graph Edit Distance Reward, which is based on the Policy Gradient and Graph Matching algorithm, to optimize neural symbolic model. In the context of text-editing image retrieval, we validate the effectiveness of our method in CSS and CRIR dataset. Besides, CRIR is a new synthetic dataset generated by us, which we will publish soon for future use. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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