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Visual exploration of urban functional zones based on augmented nonnegative tensor factorization

JOURNAL OF VISUALIZATION 2021年第2期24卷 331-347页

作者： Liu, Liyan Zhang, Hongxin Liu, Jiaxin Liu, Shangching Chen, Wei Man, Jiaju Hunan Normal Univ State Key Lab HPC&SIP MOE China Changsha Peoples R China Hunan Normal Univ Coll Math & Stat Changsha Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China

A city contains a variety of different urban functions with referring to the purpose of land use to support the diverse needs of urban residents, such as residence, working and recreation. Exploring urban functional zones is a critical task, which provides valuable applications for business site selection, transportation management and urban planning. It is well known that location information and human mobility semantics both are significant factors for identifying urban functional zones, just considering one factor is not effective. However, most of existing techniques capture the transformation of functional zones and interpret the results based solely on location or mobility semantics, and lack the capacity to deal with the multifaceted features of urban data. To tackle these problems, in this paper, we propose an interactive visual analytics system for effectively exploring urban functional zones based on spatio-temporal OD data and Points of Interest data. We first adaptively partition the territory into region units based on adaptive blue noise sampling method, extract POI feature matrix (location information) as a prior knowledge, model multidimensional spatio-temporal OD data as a tensor for addressing multifaceted features and propose an augment tensor-based algorithm that enables users to simultaneously combine mobility semantics and inherent location information for identifying functional zones. In addition, we design a set of visual encodings to better understand and interpret the results in a visual and intuitive manner. This system has been demonstrated using two case studies with a real-world dataset of HangZhou city and domain-expert interviews.

关键词： tensor factorization Visual analytics Urban functions Spatio-temporal data

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Fast tensor factorization for Large-Scale Context-Aware Recommendation from Implicit Feedback

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IEEE TRANSACTIONS ON BIG DATA 2020年第1期6卷 201-208页

作者： Chou, Szu-Yu Jang, Jyh-Shing Roger Yang, Yi-Hsuan Acad Sinica Res Ctr IT Innovat Taipei 11529 Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei 10617 Taiwan

This paper presents a fast tensor factorization (TF) algorithm for context-aware recommendation from implicit feedback. For such a recommendation problem, the observed data indicate the (positive) association between users and items in some given contexts. For better accuracy, it has been shown essential to include unobserved data that indicate the negative user-item-context associations. As such unobserved data greatly outnumber the observed ones, for efficiency existing algorithms usually use only a small part of the unobserved data for model training. We show in this paper that it is possible, and beneficial, to use all the unobserved data in training a TF based context-aware recommender system. This is achieved by two technical innovations. First, we scrutinize the matrix computation of the closed-form solution and accelerate the computation by memorizing the repetitive computation. Second, we further boost the generalization and scalability by dropping out some pairwise interactions when updating user, item or context factors to prevent overfitting and to reduce the training time. The resulting whole-data based learning algorithm, referred to as DropTF in the paper, is efficient and scale well. Our evaluation on two small benchmark datasets and a million-scale large dataset demonstrates improved accuracy over some existing algorithms for context-aware recommendation.

关键词： Recommender system collaborative filtering context-aware recommendation tensor factorization implicit feedback

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Modeling user preferences using neural networks and tensor factorization model

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INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2019年 45卷 132-148页

作者： Taneja, Anu Arora, Anuja Jaypee Inst Informat Technol Dept Comp Sci & Engn A-10Sect 62 Noida 201309 Uttar Pradesh India

With the expansion of information on the web, recommendation systems have become one of the most powerful resources to ease the task of users. Traditional recommendation systems (RS) suggest items based only on feedback submitted by users in form of ratings. These RS are not competent to deal with definite user preferences due to emerging and situation dependent user-generated content on social media, these situations are known as contextual dimensions. Though the relationship between contextual dimensions and user's preferences has been demonstrated in various studies, only a few studies have explored about prioritization of varying contextual dimensions. The usage of all contextual dimensions unnecessary raises the computational complexity and negatively influences the recommendation results. Thus, the initial impetus has been made to construct a neural network in order to determine the pertinent contextual dimensions. The experiments are conducted on real-world movies data-LDOS CoMoDa dataset. The results of neural networks demonstrate that contextual dimensions have a significant effect on user's preferences which in turn exerts an intense impact on the satisfaction level of users. Finally, tensor factorization model is employed to evaluate and validate accuracy by including neural network's identified pertinent dimensions which are modeled as tensors. The result shows improvement in recommendation accuracy by a wider margin due to the inclusion of the pertinent dimensions in comparison to irrelevant dimensions. The theoretical and managerial implications are discussed.

关键词： Context-aware recommendations Contextual modeling Consumer satisfaction level Dimensionality reduction Neural networks tensor factorization

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Content-Based Tag Propagation and tensor factorization for Personalized Item Recommendation Based on Social Tagging

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2014年第4期3卷 1–27页

作者： Rafailidis, Dimitrios Axenopoulos, Apostolos Etzold, Jonas Manolopoulou, Stavroula Daras, Petros Informat Technol Inst Ctr Res & Technol Hellas 6th Km Charilou Thermi Thessaloniki Greece Fulda Univ Appl Sci D-36039 Fulda Germany

In this article, a novel method for personalized item recommendation based on social tagging is presented. The proposed approach comprises a content-based tag propagation method to address the sparsity and "cold start" problems, which often occur in social tagging systems and decrease the quality of recommendations. The proposed method exploits (a) the content of items and (b) users' tag assignments through a relevance feedback mechanism in order to automatically identify the optimal number of content-based and conceptually similar items. The relevance degrees between users, tags, and conceptually similar items are calculated in order to ensure accurate tag propagation and consequently to address the issue of "learning tag relevance." Moreover, the ternary relation among users, tags, and items is preserved by performing tag propagation in the form of triplets based on users' personal preferences and "cold start" degree. The latent associations among users, tags, and items are revealed based on a tensor factorization model in order to build personalized item recommendations. In our experiments with real-world social data, we show the superiority of the proposed approach over other state-of-the-art methods, since several problems in social tagging systems are successfully tackled. Finally, we present the recommendation methodology in the multimodal engine of I-SEARCH, where users' interaction capabilities are demonstrated.

关键词： Algorithms Performance Social tagging recommender systems tensor factorization tag propagation content-based information retrieval relevance feedback

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Bayesian Low-Tubal-Rank Robust tensor factorization with Multi-Rank Determination

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第1期43卷 62-76页

作者： Zhou, Yang Cheung, Yiu-ming Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200062 Peoples R China Hong Kong Baptist Univ Inst Res & Continuing Educ Kowloon Tong Hong Kong Peoples R China

Robust tensor factorization is a fundamental problem in machine learning and computer vision, which aims at decomposing tensors into low-rank and sparse components. However, existing methods either suffer from limited modeling power in preserving low-rank structures, or have difficulties in determining the target tensor rank and the trade-off between the low-rank and sparse components. To address these problems, we propose a fully Bayesian treatment of robust tensor factorization along with a generalized sparsity-inducing prior. By adapting the recently proposed low-tubal-rank model in a generative manner, our method is effective in preserving low-rank structures. Moreover, benefiting from the proposed prior and the Bayesian framework, the proposed method can automatically determine the tensor rank while inferring the trade-off between the low-rank and sparse components. For model estimation, we develop a variational inference algorithm, and further improve its efficiency by reformulating the variational updates in the frequency domain. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed method in multi-rank determination as well as its superiority in image denoising and background modeling over state-of-the-art approaches.

关键词： Robust PCA tensor factorization tubal rank multi-rank determination Bayesian inference

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IMD-MTFC: Image-Domain Material Decomposition via Material-Image tensor factorization and Clustering for Spectral CT

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IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023年第4期7卷 382-393页

作者： Wang, Shaoyu Cai, Ailong Wu, Weiwen Zhang, Tao Liu, Fenglin Yu, Hengyong PLA Strateg Support Force Informat Engn Univ Henan Key Lab Imaging & Intelligent Proc Zhengzhou 450001 Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing 400044 Peoples R China Sun Yat Sen Univ Sch Biomed Engn Shenzhen 518107 Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing 400044 Peoples R China Univ Massachusetts Lowell Dept Elect & Comp Engn Lowell MA 01854 USA

Spectral computed tomography (CT) provides multispectral X-ray information that can be used for quantitative material-specific imaging compared to the conventional CT. However, the low-count photon rate in a single energy bin may lead to highly noisy measurements with compromised material contrast and accuracy. Moreover, the complicated material decomposition process is an ill-posed inverse problem, which is sensitive to noise. In this work, we develop an image-domain material decomposition method via material-image tensor factorization and clustering (IMD-MTFC) for spectral CT to obtain high-precision material-specific images. Specifically, a set of image patches is extracted from the normalized material-specific image tensors decomposed by the direct inversion (DI). Then, each of them is clustered in a given nonlocal neighboring area to explore the nonlocal self-similarity of material-specific images. Furthermore, the low-rank regularized Kronecker-basis-representation tensor factorization is employed to incorporate the sparsity and redundant correlation across the material-specific images. The split-Bregman is employed to optimize the model by dividing it into several subproblems. The performance of our method is validated with numerically simulated mouse projections, physical phantom, and preclinical experiments. The results confirm that the IMD-MTFC method outperforms other state-of-the-art competing methods.

关键词： Computed tomography tensors Image reconstruction Image edge detection Biomedical measurement X-ray imaging Spectral analysis Image domain low rank material decomposition spectral computed tomography (CT) tensor factorization

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Visual analytics of bike-sharing data based on tensor factorization

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JOURNAL OF VISUALIZATION 2018年第3期21卷 495-509页

作者： Yan, Yuyu Tao, Yubo Xu, Jin Ren, Shuilin Lin, Hai Zhejiang Univ State Key Lab CAD&CG Hangzhou Zhejiang Peoples R China

Bike-sharing systems have grown tremendously worldwide in the recent years. Understanding the user activities in urban areas is invaluable, especially for bike rebalance and urban planning. However, it is difficult to directly capture the user activity patterns from the bike-sharing data due to its sparse and discontinuous characteristics. In the recent years, many methods have been explored to visualize the user activity patterns. Many previous methods focused on visually presenting the temporal and spatial distribution directly. In this paper, we construct a tensor based on the spatial, temporal, and user information of the bike-sharing data, and employ tensor factorization to extract latent user activity patterns. To facilitate the users to analyze and understand these patterns, a visual analytics system is designed to interactively explore these patterns from the spatial, temporal, and user dimensions and compare these patterns in/between cities. We demonstrate the effectiveness of our system via case studies with real-word datasets.

关键词： Bike-sharing systems Visual analytics tensor factorization

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Missing value imputation for breast cancer diagnosis data using tensor factorization improved by enhanced reduced adaptive particle swarm optimization

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2019年第3期31卷 287-294页

作者： Nekouie, Atefeh Moattar, Mohammad Hossein Islamic Azad Univ Mashhad Branch Dept Software Engn Mashhad Razavi Khorasan Iran

Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis. An important issue in this filed is that these data often include missing value which reduces the diagnostic accuracy. In this paper, a modified tensor factorization method is used for estimating the missing data. When tensor approach intends to estimate the missing value, there should be a class balance in the dataset and tensor does not properly estimate data in the case of data insufficiency. In the proposed schema, particle swarm optimization algorithm with adaptive adjustment (RAPSO) which is improved by chaotic search is used to solve this problem. In this approach, in order to inhibit the random initialization of the particle swarm algorithm, a distinctive approach is adopted and Bayesian network is used. Finally, the accuracy of disease diagnosis is estimated through different classifiers and RMSE measure. The results suggested that the proposed method is superior to other methods in terms of RMSE, accuracy, sensitivity and specificity. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： Breast cancer diagnosis tensor factorization Particle swarm optimization Bayesian networks Support vector machine Chaos

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Microscopic image restoration based on tensor factorization of rotated patches

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ARTIFICIAL LIFE AND ROBOTICS 2013年第3-4期17卷 417-425页

作者： Kouno, Masayuki Nakae, Ken Oba, Shigeyuki Ishii, Shin Kyoto Univ Grad Sch Informat Uji Kyoto 6110011 Japan Japan Sci & Technol Agcy PRESTO Saitama Japan

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.

关键词： Image processing Microscopic image tensor factorization Local feature extraction

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A hybrid Bayesian network and tensor factorization approach for missing value imputation to improve breast cancer recurrence prediction

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2019年第2期31卷 175-184页

作者： Vazifehdan, Mahin Moattar, Mohammad Hossein Jalali, Mehrdad Islamic Azad Univ Mashhad Branch Dept Software Engn Mashhad Iran

Data mining and machine learning approaches can be used to predict breast cancer recurrence. However, real datasets often include missing values for various reasons. In this paper, a hybrid imputation method is proposed with respect to the dependency between the attributes and the type of incomplete attributes in order to especially improve the prediction of breast cancer recurrence. After splitting the dataset into two discrete and numerical subsets, first missing values of the discrete fields are imputed using Bayesian network. Then, using tensor factorization, the integrated dataset, which comprises of the filled-subset of the previous stage and numerical missing values subset, is constructed so that both continuous missing values are imputed and the accuracy of imputation is enhanced. We evaluated the proposed method versus six imputation methods i.e. mean, Hot-deck, K-NN, Weighted K-NN, tensor factorization and Bayesian network on three datasets and used three classifiers, namely decision tree, K-Nearest Neighbor and Support Vector Machine for recurrence prediction. Experimental results show that the proposed method has as average 0.26 prediction improvement. Also, the prediction performance of the proposed approach outperforms all other imputation-classifier pairs in terms of specificity, sensitivity and accuracy. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： Breast cancer recurrence Missing value imputation Classification tensor factorization Bayesian network

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