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When non-local similarity meets tensor factorization: A patch-wise method for hyperspectral anomaly detection

SIGNAL PROCESSING 2025年 230卷

作者： Meng, Lixiang Xu, Yanhui Shen, Qiangqiang Chen, Yongyong Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

Hyperspectral anomaly detection (HAD) entails identifying anomaly pixels in hyperspectral images (HSI) that significantly diverge from the background spectral signatures. However, the conventional tensor-based HAD methods typically leverage tensor nuclear norm (TNN) and its variants to exploit the low-rank characteristic on the image-level HSI. Specifically, the omission of non-local similarity features in HAD reduces the potential to leverage pertinent information, leading to the loss of crucial HSI structures and details. In addition, the tensor singular value decomposition (t-SVD) involved in solving the TNN minimization tends to augment the computational time as the data size expands. To address these issues, a novel patch-wise model for HAD that integrates non-local similarity with tensor factorization (NSTF) is proposed. Specifically, instead of using the entire image, we employ aggregated non-local similarity patches to characterize the low-rank structure of HSI, accounting for both spectral correlation and non-local similarity simultaneously and equivalently. Moreover, we introduce tensor factorization to factorize the large-scale tensor generated by grouping non-local similarity patches into two small-scale tensors, which more efficiently exploits its low-rank property of background. Finally, comprehensive experimental results validated on HSI datasets unequivocally establish the superiority of our proposed method over cutting-edge methods.

关键词： Hyperspectral anomaly detection Non-local similarity tensor factorization

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Time-aware tensor factorization for temporal recommendation

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APPLIED INTELLIGENCE 2025年第1期55卷 1-16页

作者： Feng, Yali Wen, Wen Hao, Zhifeng Cai, Ruichu Guangdong Univ Technol Dept Comp Sci Guangzhou 510006 Peoples R China Shantou Univ Coll Sci Shantou 515063 Peoples R China

In recent years, temporal recommendation, which recommends items to users with considering temporal information has attracted widespread attention. How to capture and combine the time-varying user behavior distributions and the time-varying user behavior transition patterns is challenging. To address these challenges, we propose a Time-Aware tensor factorization for Temporal Recommendation (TATF4TRec). First, the personalized Markov transition tensors are applied to represent the users' temporal behaviors. Then a tensor factorization method is proposed to capture the time-varying patterns of these tensors. Furthermore, the model linearly combines the time-varying patterns of user behavior and predicts the recommended results at a given time. Extensive experiments on five datasets demonstrate that TATF4TRec outperforms the state-of-the-art baselines significantly.

关键词： Temporal recommendation Time-varying pattern tensor factorization

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tensor factorization-Based Method for tensor Completion with Spatio-temporal Characterization

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2023年第1期199卷 337-362页

作者： Yu, Quan Zhang, Xinzhen Huang, Zheng-Hai Hunan Univ Sch Math Changsha 410082 Hunan Peoples R China Tianjin Univ Sch Math Tianjin 300354 Peoples R China

In this paper, we propose a novel tensor factorization-based method for the third-order tensor completion problem with spatio-temporal characterization. For this aim, we consider tensor fibered rank, which extends tubal rank, to improve the flexibility and accuracy of data characterization. Based on this rank, we apply a factorization-based method to complete the third-order low-rank tensors with spatio-temporal characteristics, which are intrinsic features of image, video and internet traffic tensor data. The model not only makes good use of the low-rank structure of tensors, but also takes into account the spatio-temporal characteristics of the data. Finally, we report numerical results on completing image, video and internet traffic data. The results demonstrate that our method outperforms some existing methods.

关键词： tensor completion tensor factorization tensor fibered rank Spatio-temporal characteristics

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tensor factorization method based on review text semantic similarity for rating prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 629-638页

作者： Chambua, James Niu, Zhendong Yousif, Abdallah Mbelwa, Jimmy Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Univ Dar Es Salaam Comp Sci & Engn Dept POB 35091 Dar Es Salaam Tanzania

Recommendation methods have been proved to be successful in eliminating the information overload problem. However, they are still insufficient as far as data sparsity and cold start issues are concerned. Some approaches have attempted to resolve these issues by utilizing relevant information contained in user review text, although, the type of information extracted from the review text and the way such recommendation methods utilize the information affect recommendation accuracy of the models. In this paper, we address such challenges by considering linguistic similarity between review texts and employ it as additional factor in rating prediction, and we propose a recommendation method based on tensor factorization which involves review text semantic similarity. The proposed tensor factorization model supplements the central task of factorization methods of finding similar users, uncovering underlying characteristics of the data and predicting user preferences by introducing text semantic similarity. The proposed method is carried out in two main phases;first phase, by computing semantic similarity between review texts and assigning a similarity score, and second phase by introducing the similarity score as an additional factor in the probabilistic matrix factorization (PMF) model. To evaluate the performance of the proposed approach, several Amazon datasets were experimented and the results verify that the semantic similarity of review texts not only successfully portray user preferences, and extend PMF to include review texts similarities but also increases prediction influence which results in improved performance. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： tensor factorization Semantic similarity User reviews Rating prediction

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tensor factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 4888-4898页

作者： Che, Hangjun Pan, Baicheng Leung, Man-Fai Cao, Yuting Yan, Zheng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Anglia Ruskin Univ Fac Sci & Engn Cambridge CB1 1PT England Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Univ Technol Sydney Australian AI Inst Fac Engn & Informat Technol Ultimo NSW Australia

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.

关键词： Fake news detection social media mining tensor factorization

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tensor factorization and Attention-Based CNN-LSTM Deep-Learning Architecture for Improved Classification of Missing Physiological Sensors Data

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IEEE SENSORS JOURNAL 2023年第2期23卷 1286-1294页

作者： Akmal, Muhammad Riphah Int Univ Dept Biomed Engn Lahore 55150 Pakistan

Oneof the essential issues for efficient control of prosthesis is the accurate classification of target movements hidden in electroencephalography (EEG) and electromyography (EMG) signals. However, in the presence of missing data in acquired signals, the classification accuracy degrades significantly as the amount of missing data increases, reducing the control performance of the prosthesis. This research proposes a framework based on tensor (multidimensional array) factorization and attention-based convolutional neural network (CNN)-long short-termmemory (LSTM) deep learning (DL) for recovering missing data and performing classification of target movements, respectively. To recover missing data in tensor factorization, Canonical/Polyadic Weighted OPTimization (CP-WOPT) is employed, and its performance is compared to state-of-the-art factorization methods, whereas the performance of CNN-LSTM-attention layer (Attn) is compared to state-of-the-art machine learning and DL classifiers. Results show that CNN-LSTM-Attn obtained the mean classification accuracy of 98%, 83%, and 90% on complete (0% missing data), partially complete (10% to 50% missing data), and tensor-recovered real-world EEG and EMG data, respectively, demonstrating the applicability of the proposed framework.

关键词： Attention mechanism classification convolutional neural network (CNN) deep learning (DL) long short-term memory (LSTM) missing data tensor factorization

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tensor factorization for Low-Rank tensor Completion

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第3期27卷 1152-1163页

作者： Zhou, Pan Lu, Canyi Lin, Zhouchen Zhang, Chao Peking Univ Sch Elect Engn & Comp Sci Key Lab Machine Percept Beijing 100871 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117583 Singapore Cooperat Medianet Innovat Ctr Shanghai 200240 Peoples R China

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.

关键词： tensor factorization tensor completion low-rank factorization

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tensor factorization with Total Variation and Tikhonov Regularization for Low-Rank tensor Completion in Imaging Data

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2020年第6-7期62卷 900-918页

作者： Lin, Xue-Lei Ng, Michael K. Zhao, Xi-Le Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China Univ Hong Kong Dept Math Pokfulam Hong Kong Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China

The main aim of this paper is to study tensor factorization for low-rank tensor completion in imaging data. Due to the underlying redundancy of real-world imaging data, the low-tubal-rank tensor factorization (the tensor-tensor product of two factor tensors) can be used to approximate such tensor very well. Motivated by the spatial/temporal smoothness of factor tensors in real-world imaging data, we propose to incorporate a hybrid regularization combining total variation and Tikhonov regularization into low-tubal-rank tensor factorization model for low-rank tensor completion problem. We also develop an efficient proximal alternating minimization (PAM) algorithm to tackle the corresponding minimization problem and establish a global convergence of the PAM algorithm. Numerical results on color images, color videos, and multispectral images are reported to illustrate the superiority of the proposed method over competing methods.

关键词： tensor factorization Hybrid regularization tensor completion Proximal alternating minimization

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tensor factorization and Spin construction for Kac-Moody algebras

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ADVANCES IN MATHEMATICS 2009年第5期222卷 1649-1686页

作者： Walia, Rajeev Michigan State Univ Dept Math E Lansing MI 48824 USA

In this paper we discuss the "factorization phenomenon" which occurs when a representation of a Lie algebra is restricted to a subalgebra, and the result factors into a tensor product of smaller representations of the subalgebra. We analyze this phenomenon for symmetrizable Kac-Moody algebras (including finite-dimensional, semi-simple Lie algebras). We present a few factorization results for a general embedding of a symmetrizable Kac-Moody algebra into another and provide an algebraic explanation for such a phenomenon using Spin construction. We also give some application of these results for semi-simple, finite-dimensional Lie algebras. We extend the notion of Spin functor from finite-dimensional to symmetrizable Kac-Moody algebras, which requires a very delicate treatment. We introduce a certain category of orthogonal g-representations for which, surprisingly, the Spin functor gives a g-representation in Bernstein-Gelfand-Gelfand category O. Also, for an integrable representation, Spin produces an integrable representation. We give the formula for the character of Spin representation for the above category and work out the factorization results for an embedding of a finite-dimensional, semi-simple Lie algebra into its untwisted affine Lie algebra. Finally, we discuss the classification of those representations for which Spin is irreducible. (C) 2009 Elsevier Inc. All rights reserved.

关键词： tensor factorization Spin construction Kac-Moody algebras

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tensor factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction

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JOURNAL OF CARDIOVASCULAR TRANSLATIONAL RESEARCH 2017年第3期10卷 305-312页

作者： Luo, Yuan Ahmad, Faraz S. Shah, Sanjiv J. Northwestern Univ Feinberg Sch Med Dept Prevent Med 11th FloorArthur Rubloff Bldg Chicago IL 60611 USA Northwestern Univ Dept Med Div Cardiol Feinberg Sch Med Chicago IL 60611 USA

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies. tensor factorization also allows for better interpretability by supporting dimensionality reduction and identifying latent groups of data for meaningful summarization of both features and disease outcomes. In this narrative review, we analyze the modest literature on the application of tensor factorization to related biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest multiple tensor factorization formulations capable of integrating the deep phenotypic and trans-omic modalities of data for HFpEF, or accounting for interactions between genetic variants at different omic hierarchies. We encourage extensive experimental studies to tackle challenges in applying tensor factorization for precision medicine in HFpEF, including effectively incorporating existing medical knowledge, properly accounting for uncertainty, and efficiently enforcing sparsity for better interpretability.

关键词： tensor factorization Precision medicine Heart failure with preserved ejection fraction Machine learning Big data Trans-omics

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