Article
Engineering, Mechanical
Mateusz Gabor, Rafal Zdunek, Radoslaw Zimroz, Jacek Wodecki, Agnieszka Wylomanska
Summary: In this study, a novel non-negative tensor factorization (NTF)-based method is proposed for vibration-based local damage detection in rolling element bearings. The time-frequency method is used to decompose the non-stationary diagnostic signal from faulty machines. Multi-linear NTF-based components are extracted from a 3D array of time-frequency representations of the observed signal, allowing for efficient separation of informative and non-informative components. Experiments on synthetic and real signals demonstrate the high efficiency of this method compared to the existing non-negative matrix factorization approach.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Khanh Luong, Richi Nayak, Thirunavukarasu Balasubramaniam, Md Abul Bashar
Summary: This paper proposes a deep non-negative matrix factorization-based framework for effective multi-view data clustering by uncovering the non-linear relationships and intrinsic components of the data. The framework effectively incorporates the optimal manifold of multi-view data and outperforms existing multi-view matrix factorization-based methods.
PATTERN RECOGNITION
(2022)
Article
Automation & Control Systems
Zhiwei Xing, Meng Wen, Jigen Peng, Jinqian Feng
Summary: The paper introduces a novel discriminative semi-supervised NMF (DSSNMF) algorithm that effectively utilizes label information from a portion of the data, with empirical experiments demonstrating its effectiveness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Biotechnology & Applied Microbiology
Ko Abe, Masaaki Hirayama, Kinji Ohno, Teppei Shimamura
Summary: The BALSAMICO hierarchical Bayesian framework accurately estimates parameters needed to analyze the connections between microbial community systems and their environments, and effectively detects these communities in real-world circumstances.
Article
Computer Science, Artificial Intelligence
Ping Deng, Fan Zhang, Tianrui Li, Hongjun Wang, Shi-Jinn Horng
Summary: Clustering remains a challenging research hotspot in data mining. This paper proposes a biased unconstrained non-negative matrix factorization (BUNMF) model to improve the clustering performance. The model modifies the update rules and adds bias, and introduces three different activation functions for iteration updates.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Guangfu Chen, Haibo Wang, Yili Fang, Ling Jiang
Summary: This paper proposes a novel link prediction model based on deep non-negative matrix factorization, which combines topology and sparsity-constrained to perform link prediction tasks. Extensive experiments demonstrate that the proposed model significantly outperforms existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Ping Deng, Tianrui Li, Dexian Wang, Hongjun Wang, Hong Peng, Shi-Jinn Horng
Summary: Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. However, the optimization method using the Karush-Kuhn-Tucker (KKT) conditions is poorly scalable. In this study, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model, which decouples the elements of the matrix and combines them with a non-linear mapping function in a non-negative value domain. The objective function is optimized using the stochastic gradient descent (SGD) algorithm, and three uNMFMvC methods are constructed based on different mapping functions.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Mathematics
Guowei Yang, Lin Zhang, Minghua Wan
Summary: This study proposed an exponential graph regularization non-negative low-rank factorization algorithm to improve the performance and robustness of NMF. By applying low-rank constraint, non-negative factorization, and graph embedding with matrix exponentiation, it aims to learn undisturbed latent data representations while preserving the local structure of known samples.
Article
Mathematics
Minghua Wan, Mingxiu Cai, Guowei Yang
Summary: In this paper, a new algorithm named robust exponential graph regularized non-negative matrix factorization (REGNMF) is proposed to address the possible singular matrix problem in GNMF. By adding a matrix exponent to the regularizer, the singular matrix is transformed into a non-singular matrix. The REGNMF method is iteratively solved using a multiplicative non-negative updating rule. Experimental results on various datasets show that the proposed method is more significant.
Article
Computer Science, Artificial Intelligence
Gal Gilad, Itay Sason, Roded Sharan
Summary: Non-negative matrix factorization (NMF) is a popular method used to find low rank approximations of matrices, especially in genomics for interpreting mutation data. A key challenge in using NMF is determining the number of components. A new method, CV2K, is proposed in this study to automatically select this number based on cross validation and parsimony considerations. Results show that CV2K leads to improved predictions compared to previous approaches, even those involving human assessment.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Yang Zhao, Furong Deng, Jihong Pei, Xuan Yang
Summary: This article proposes a novel progressive deep non-negative matrix factorization (PDNMF) architecture that enhances feature representation by adding a basis image reconstruction step. The evaluation shows that the proposed method outperforms other deep factorization methods in image recognition.
PATTERN RECOGNITION
(2022)
Article
Mathematics
Xindi Ma, Jie Gao, Xiaoyu Liu, Taiping Zhang, Yuanyan Tang
Summary: This work introduces a new probabilistic non-negative matrix factorization method that factors a non-negative matrix into a low-rank factor matrix with 0,1 constraints and a non-negative weight matrix. By utilizing deterministic Indian buffet process variational inference to automatically learn potential binary features and feature numbers, the method achieves an efficient approach for matrix factorization. The proposed method exhibits efficacy in comparative experiments on synthetic and real-world datasets.
Article
Computer Science, Artificial Intelligence
Lin Feng, Wenzhe Liu, Xiangzhu Meng, Yong Zhang
Summary: This paper introduces an innovative multi-view clustering method, SMCTN, which utilizes triplex regularized non-negative matrix factorization to effectively extract multi-view information while maintaining low-dimensional geometry structure. Extensive experimental results on textual and image datasets demonstrate the superior performance of the proposed method.
Article
Physics, Multidisciplinary
Srilatha Tokala, Murali Krishna Enduri, T. Jaya Lakshmi, Hemlata Sharma
Summary: Matrix factorization is a method for extracting valuable recommendations from complex networks, but it requires extensive computational resources. To overcome this challenge, we propose a community-based matrix factorization approach that divides the network into communities and applies matrix factorization on these communities to improve the quality of recommendations.
Article
Engineering, Multidisciplinary
Hao Jiang, Ming Yi, Shihua Zhang
Summary: This study introduces a novel approach based on kernel non-negative matrix factorization to detect nonlinear gene-gene relationships and build a low-dimensional representation on the original data. Furthermore, an efficient method for determining the optimal cluster number is proposed to improve clustering accuracy.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.