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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Rapid diagnosis of osteoarthritis using serum Raman spectroscopy based on MFCC transformation combined with MCNN 4

Rapid diagnosis of osteoarthritis using serum Raman spectros...

引用

4th International Conference on Optics and Image processing, ICOIP 2024

作者： Xu, Hongze Chen, Chen Tian, Xuecong He, Liang Zuo, Enguang Chen, Xiaomei Wu, Lijun Chen, Cheng Lv, Xiaoyi College of Software Xinjiang University Urumqi830046 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Karamay834099 China Department of Rheumatology and Immunology People's Hospital of Xinjiang Uygur Autonomous Region Urumqi830001 China Xinjiang Clinical Research Center for Rheumatoid Arthritis Urumqi830001 China Department of Electronic Engineering Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China School of Information Science and Engineering Xinjiang University Ürümqi China

ISBN: (纸本)9781510682481

Osteoarthritis (OA) is a common chronic degenerative disease worldwide. Currently, traditional diagnostic methods for OA have disadvantages such as high cost and long time consumption. For this reason, it is urgent to have a method that can diagnose osteoarthritis quickly and accurately. During this study, we suggest a multi-column convolutional neural networks (MCNN) model based on Mel-frequency cepstral coefficient (MFCC). We first compare the effectiveness of Partial Least Squares (PLS) and MFCC in preprocessing the data and comprehensively evaluate the performance of the model by five-fold cross-validation. To further improve the ability of recognizing spectral features, We utilize the Spectral Segmentation Method (SSM) to construct the MFCC feature sequences into 2D spectral feature maps and input them into the MCNN for classification. The final experimental results show that our proposed mfc - ssm - mcnn model has a high classification efficiency in diagnosing osteoarthritis with an accuracy of 95.0%. © 2024 SPIE.

关键词： Raman spectroscopy

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A Multiple Instance Learning Algorithm Using Graph Convolutional Network for Speech Content Classification 5

A Multiple Instance Learning Algorithm Using Graph Convoluti...

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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Zhang, Jianyi Yao, Jiaqi Chu, Yanjie Yan, Jikun PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781665415989

Recently, speech content classification begins to be formulated as a multiple instance learning (MIL) problem by some researchers. Under the MIL framework, a speech signal is considered as a bag with labels, and speech fragments are regarded as instances without corresponding labels. On this basis, we propose a new algorithm, named MI-CNN-G, for speech content classification using Graph Convolutional Network (GCN) under the MIL framework. First, we only train the MIL classifier, whose architecture is based on Convolutional Neural Network, for bag classification. Second, we utilize GCN to handle graphs composed of instance features that are the outputs of the MIL classifier for correcting the predicted probabilities of the classifier and then updated the classifier and GCN together. Finally, we employ the MI-CNN-G model to predict the labels of unknown bags. The experimental results show that GCN is effective in improving the performance of the MIL classifier on speech content classification and the MI-CNN-G model outperforms the baseline approaches in the Average Precision metric. © 2021 IEEE.

关键词： Convolutional neural networks

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Reduced-complexity Transmit Antenna Selection for Spatial Modulation 20

Reduced-complexity Transmit Antenna Selection for Spatial Mo...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Zhu, Ruotong Chen, Hao Xiao, Yue Ji, Xinrong Tang, Wenqi Zhao, Yan Uestc National Key Laboratory of Science and Technology on Communications Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728181417

In this paper, a novel transmit antenna selection (TAS) scheme is conceived for spatial modulation multiple-input multiple-output (SM-MIMO) systems, in order to reduce the search complexity. Compared to the existing TAS schemes, the proposed scheme shows a similar performance to optimal one with lower complexity, by exploring an efficient pruning algorithm for tree search based on a combined criterion. Simulation result indicates that the proposed scheme is capable of balancing the transmission performance and search complexity. © 2020 IEEE.

关键词： MIMO systems

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Performance Analysis of OSM-OFDM Systems with Carrier Frequency Offset 20

Performance Analysis of OSM-OFDM Systems with Carrier Freque...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Jiang, Tingmin Xiao, Yue Dan, Lilin Ji, Xinrong Tang, Wenqi Zhao, Yan Uestc National Key Laboratory of Science and Technology on Communications Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728181417

Offset spatial modulation (OSM) is a novel modulation scheme with simplified radio-frequency-switching structure. In this paper, the concept of OSM is combined with orthogonal frequency division multiplexing (OFDM) transmission to take advantage of the benefits of these two techniques. We shed light on the effects of carrier frequency offset (CFO) on the OSM-OFDM systems over multipath Rayleigh fading channel, where the inter-carrier interference (ICI) induced by CFO degrades the system performance. We further derive the theoretical bit-error rate (BER) upper bound of OSM-OFDM impaired by CFO. Simulation results show that the theoretical BER is in good agreement with its simulated counterpart, which demonstrates the accuracy of the theoretical analysis. © 2020 IEEE.

关键词： Orthogonal frequency division multiplexing

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Power Allocation for Multiple Sensing and Communication Tasks Using Massive MIMO Base Station

Power Allocation for Multiple Sensing and Communication Task...

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International Conference on Control, Automation and Information sciences (ICCAIS)

作者： Ziyi Cao Xuanqi Wang Ye Yuan Wei Yi Junkun Yan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Glasgow College University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

In this paper, we consider a joint sensing and communication scenario with the simultaneous implementation of multiple sensing and communication tasks employing a massive multiple-input multiple-output (MIMO) base station system. This system operates on orthogonal frequency-division multiplexing (OFDM) modulations for both communication and sensing signals. To evaluate the performance of dual functions, we derive the lower bound on the achievable downlink rate for communication and signal-to-interference-plus-noise ratio (SINR) for radar detection. By focusing on the power allocation strategy, we propose two optimization cases with different emphasis on sensing and communication to explore the trade-off between radar performance and communication quality. The formulated optimization problem can be effectively solved via the binary search technique, and simulation results demonstrate its efficiency.

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A Novel Postprocessing Method for Ship Detection in SAR Images Using Greedy Strategies

A Novel Postprocessing Method for Ship Detection in SAR Imag...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Yunfeng Liu Tianyi Car Xiaonan Wu Tong Gu Linglong Zhang Yachao Li HangZhou Institute of Technology Xian University HangZhou China Institute of the second Academy of Aerospace Science and industry Beijing China National Key Laboratory of Radar Signal Processing Xian University Xian China

This article proposes a novel post-processing method for ship detection in synthetic aperture radar (SAR) images, which addresses the issue of head to tail separation that cannot be avoided when using morphological compensation to process candidate regions generated by the constant false alarm detection (CFAR). This algorithm first constructs a connected domain for the candidate regions generated by CFAR, uses a greedy strategy to update the centroid and size of the connected domain, and finally unreasonable candidate connected domains are removed to eliminate high noise sea clutter and large volume non ship targets. Compared to the morphological compensation post-processing method, the greedy strategy the greedy strategy fully utilizes the prior knowledge of ship data in the post-processing of constant false alarm ship detection results, overcomes the disadvantage of complex morphological compensation methods that require fine-tuning parameters in post-processing, and completely solves the problem of head tail separation that cannot be completely solved, improving the accuracy of synthetic aperture radar ship detection. The effectiveness and robustness of our method have been demonstrated by the data from the domestically produced Gaofen 3 satellite and the data from the European Space Agency's TerraSAR-X satellite.

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Application of complex deep neural networks in complex data processing and comparative analysis of activation functions

Application of complex deep neural networks in complex data ...

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IET International Radar Conference (IRC 2023)

作者： Junxiang Yang Ying Zhou Hui Ma Kun Lv Hongwei Liu Lin Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China Beijing Institute of Remote-sensing Equipment Science and Technology on Millimeter-wave Laboratory Beijing People's Republic of China

ISBN: (数字)9781837240982

The application of deep learning to fast radar data processing has important significance. The effectiveness of complex deep neural networks (CDNNs) has recently been demonstrated, and complex networks are important for processing complex data because they have the ability to handle phases. And the complex-valued activation function introduces a nonlinear transformation to the network, which has a great potential to improve the network performance. In this paper, we test the feasibility of CDNNs and the effect of different activation functions in the framework of complex networks under the goal of retaining more phase information. In particular, we propose a new complex-valued activation RℂReLU based on RℂeLu and test its performance in a classification and auto-encoder context. Finally, we perform radar imaging reconstruction by different activation functions in complex networks, and the results show that RℂReLU is more suitable for processing radar data in CDNNs.

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

arXiv

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arXiv 2024年

作者： Sun, Dengdi Pan, Yajie Lu, Andong Li, Chenglong Luo, Bin The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to incorporate temporal information, they have the risk of disrupting the original target appearance and accumulating errors over time. To alleviate these limitations, we propose a novel Transformer RGBT tracking approach, which mixes spatio-temporal multimodal tokens from the static multimodal templates and multimodal search regions in Transformer to handle target appearance changes, for robust RGBT tracking. We introduce independent dynamic template tokens to interact with the search region, embedding temporal information to address appearance changes, while also retaining the involvement of the initial static template tokens in the joint feature extraction process to ensure the preservation of the original reliable target appearance information that prevent deviations from the target appearance caused by traditional temporal updates. We also use attention mechanisms to enhance the target features of multimodal template tokens by incorporating supplementary modal cues, and make the multimodal search region tokens interact with multimodal dynamic template tokens via attention mechanisms, which facilitates the conveyance of multimodal-enhanced target change information. Our module is inserted into the transformer backbone network and inherits joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three RGBT benchmark datasets show that the proposed approach maintains competitive performance compared to other state-of-the-art tracking algorithms while running at 39.1 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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