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Multiview feature selection combining latent space and similarity structure learning

Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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Semi-supervised recurrent complex-valued convolution neural network for polsar image classification 39

Semi-supervised recurrent complex-valued convolution neural ...

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39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

作者： Zhao, Feng Ma, Gaini Xie, Wen Liu, Hanqiang Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Xi'an710121 China School of Communications and Information Engineering Xi'An University of Posts and Telecommunications Xi'an710121 China School of Computer Science Shaanxi Normal University Xi'an710119 China

This paper presents a novel semi-supervised terrain classification method of polarimetric synthetic aperture radar (PolSAR) image based on complex-valued convolution neural network (CV-CNN). Our proposed method only needs a small number of labeled samples to achieve good classification results. First, a Wishart classifier is used to find highly reliable samples in PolSAR data. Then, a new semi-supervised deep recurrent CV-CNN (RCVCNN) classification model has been proposed to improve PolSAR image classification accuracy and effectively solve network overfitting. Finally, a real PolSAR dataset is used to verify the effectiveness of our algorithm. Compared with the other three state-of-the-art methods, the proposed one show improvements in accuracy and better consistency. © 2019 IEEE.

关键词： Image classification

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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2019 IEEE International Conference on engineering, Technology and Education, TALE 2019

作者： Sulistiyo, Edy Vindy Anwar, Nizirwan Warnars, Diana Teresia Spits Wibawa, Setya Chendra Brotosaputro, Goenawan Kristiadi, Dedy Prasetya Warnars, Harco Leslie Hendric Spits Hashimoto, Kiyota Universitas Negeri Surabaya Electrical Engineering Department Jawa Timur60213 Indonesia School of Computer Science Bina Nusantara University Game Application Technology Program Jakarta11480 Indonesia Esa Unggul University Faculty of Computer Science Jakarta11510 Indonesia Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Budi Luhur Information System Department Jakarta12260 Indonesia Raharja University Computer Science Department Tangerang15117 Indonesia Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Prince Songkla University Faculty of Technology and Environment Phuket Campus Thailand

ISBN: (纸本)9781728126654

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3. © 2019 IEEE.

关键词： Serious games

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LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention

LiDAR-Based Online 3D Video Object Detection With Graph-Base...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Junbo Yin Jianbing Shen Chenye Guan Dingfu Zhou Ruigang Yang Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China Baidu Research Inception Institute of Artificial Intelligence UAE National Engineering Laboratory of Deep Learning Technology and Application China University of Kentucky Kentucky USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences. The proposed model comprises a spatial feature encoding component and a spatiotemporal feature aggregation component. In the former component, a novel Pillar Message Passing Network (PMPNet) is proposed to encode each discrete point cloud frame. It adaptively collects information for a pillar node from its neighbors by iterative message passing, which effectively enlarges the receptive field of the pillar feature. In the latter component, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU) to aggregate the spatiotemporal information, which enhances the conventional ConvGRU with an attentive memory gating mechanism. AST-GRU contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module, which can emphasize the foreground objects and align the dynamic objects, respectively. Experimental results demonstrate that the proposed 3D video object detector achieves state-of-the-art performance on the large-scale nuScenes benchmark.

关键词： Three-dimensional displays Feature extraction Spatiotemporal phenomena Detectors Object detection Encoding Message passing

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A Noble Genetic Algorithm to Solve a Solid Green Traveling Purchaser Problem with Uncertain Cost Parameters

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American Journal of Mathematical and Management sciences 2020年第1期40卷 17-31页

作者： Roy, Arindam Gao, Rong Jia, Lifen Maity, Samir Kar, Samarjit Department of Computer Science and Application Prabhat Kumar College West Bengal India School of Economics and Management Hebei University of Technology Tianjin China School of Management and Engineering Capital University of Economics and Business Beijing China OM Group Indian Institute of Management Calcutta Joka India Department of Mathematics NIT Durgapur Durgapur India

The traveling purchaser problem (TPP) is a notable generalization of the traveling salesman problem (TSP) which involves selecting a subset of markets at a minimum traveling cost such that the demand for each product is satisfied. A solid green traveling purchaser problem (SGTPP) is a TPP in which, at each market, some conveyances are available to travel to another market with minimum cost considering the environmental impact caused by carbon emission. In this paper, we formulate an SGTPP with travel cost between each pair of markets and purchase price of the products as uncertain variables. Using uncertainty theory, an expected value model is formulated and then transformed into the corresponding deterministic form. Finally, a noble genetic algorithm (nGA) is designed to solve the proposed model. The algorithm is called noble because it adopts a crossover of the combination of a probabilistic selection of three parents, according to real-life In Vitro Fertilization (IVF) techniques. Computational results reveal that our proposal is favorably compared to previous algorithms in the existing literature. © 2020 Taylor & Francis Group, LLC.

关键词： Genetic algorithms

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ADAPTIVE PROMPTNET FOR AUXILIARY GLIOMA DIAGNOSIS WITHOUT CONTRAST-ENHANCED MRI

arXiv

引用

arXiv 2022年

作者： Wang, Yeqi Huang, Weijian Li, Cheng Zheng, Xiawu Lin, Yusong Wang, Shanshan School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China School of Cyber Science and Engineering Zhengzhou University Henan Zhengzhou China Collaborative Innovation Center for Internet Healthcare Zhengzhou University Henan Zhengzhou China Hanwei IoT Institute Zhengzhou University Henan Zhengzhou China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangdong Guangzhou China

Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most of the existing relevant studies, in which remarkable diagnosis results have been reported. Nevertheless, acquiring contrast-enhanced MRI data is sometimes not feasible due to the patient’s physiological limitations. Furthermore, it is more time-consuming and costly to collect contrast-enhanced MRI data in the clinic. In this paper, we propose an adaptive PromptNet to address these issues. Specifically, a PromptNet for glioma grading utilizing only non-enhanced MRI data has been constructed. PromptNet receives constraints from features of contrast-enhanced MR data during training through a designed prompt loss. To further boost the performance, an adaptive strategy is designed to dynamically weight the prompt loss in a sample-based manner. As a result, PromptNet is capable of dealing with more difficult samples. The effectiveness of our method is evaluated on a widely-used BraTS2020 dataset, and competitive glioma grading performance on NE-MRI data is *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Grading

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A Methodology for Reliability of WSN Based on Software Defined Network in Adaptive Industrial Environment

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 74-82页

作者： Ying Duan Wenfeng Li Xiuwen Fu Yun Luo Lin Yang School of Logistics Engineering Wuhan Universityof Technology Wuhan 430063 China Department of Computer Science and Application Zhengzhou University of AeronauticsZhengzhou 450015 China School of Logistics Engineering Wuhan University of Technology Wuhan 430063 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 201306 China IEEE

As communication technology and smart manufacturing have developed, the industrial internet of things(IIo T)has gained considerable attention from academia and *** sensor networks(WSNs) have many advantages with broad applications in many areas including environmental monitoring, which makes it a very important part of IIo T. However,energy depletion and hardware malfunctions can lead to node failures in WSNs. The industrial environment can also impact the wireless channel transmission, leading to network reliability problems, even with tightly coupled control and data planes in traditional networks, which obviously also enhances network management cost and complexity. In this paper, we introduce a new software defined network(SDN), and modify this network to propose a framework called the improved software defined wireless sensor network(improved SD-WSN). This proposed framework can address the following issues. 1) For a large scale heterogeneous network, it solves the problem of network management and smooth merging of a WSN into IIo T. 2) The network coverage problem is solved which improves the network reliability. 3) The framework addresses node failure due to various problems, particularly related to energy ***, it is necessary to improve the reliability of wireless sensor networks, by developing certain schemes to reduce energy consumption and the delay time of network nodes under IIo T conditions. Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN.

关键词： Industrial internet of things(IIo T) reliability software defined network(SDN) wireless sensor network(WSN)

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A mutual bootstrapping model for automated skin lesion segmentation and classification

arXiv

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arXiv 2019年

作者： Xie, Yutong Zhang, Jianpeng Xia, Yong Shen, Chunhua National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'an710072 China School of Computer Science University of Adelaide SA Australia

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhancedSN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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Illuminating Recommendation by Understanding the Explicit Item Relations

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Journal of computer science & Technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability

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