Article
Computer Science, Software Engineering
Yong Sheng Soh, Antonios Varvitsiotis
Summary: This paper introduces the application of the symmetric-cone multiplicative update algorithm to the cone factorization problem in the case of symmetric cones. The proposed algorithm updates each iterate by applying a chosen automorphism of the cone, ensuring that iterates remain within the interior of the cone. The algorithm utilizes a generalization of the geometric mean on symmetric cones. It has important applications in computing nonnegative matrix factorizations and hybrid lifts.
MATHEMATICAL PROGRAMMING
(2023)
Article
Engineering, Biomedical
Lingfeng Xu, Maria Elena Chavez-Echeagaray, Visar Berisha
Summary: An unsupervised channel selection framework based on semi-nonnegative matrix factorization (semi-NMF) is proposed for efficient and accurate emotion recognition. The framework can effectively detect brain regions active during emotional activities and achieve better recognition performance by using a reduced set of channels compared to using all channels.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Review
Computer Science, Artificial Intelligence
Wen-Sheng Chen, Kexin Xie, Rui Liu, Binbin Pan
Summary: This paper focuses on the theoretical idea, basic model, optimization method, and variants of symmetric non-negative matrix factorization (SNMF), a promising tool for data analysis. It classifies SNMF-related approaches into classic SNMFs and extended SNMFs, elaborates on key concepts, characteristics, and current issues of these algorithms, and compares the clustering performance and algorithm effects of SNMF and its variants on object image datasets. Additionally, it compares the performance of similarity matrix construction methods and discusses open problems with SNMF.
Article
Computer Science, Artificial Intelligence
Zhiyuan Zou, Weibin Liu, Weiwei Xing
Summary: This paper proposes a novel fusion framework of adaptive nonnegative feature fusion for scene classification, which integrates nonnegative matrix factorization, adaptive feature fusion, and feature fusion boosting. Experiments demonstrate that the proposed method can efficiently handle multi-class scene problems and achieve remarkable classification performance.
PATTERN RECOGNITION
(2022)
Article
Chemistry, Analytical
Lin Liang, Xingyun Ding, Fei Liu, Yuanming Chen, Haobin Wen
Summary: A new method based on KNMF is proposed to extract fault features from the amplitude spectrogram, showing very high performance.
Article
Computer Science, Artificial Intelligence
Ling Zhang, Wenchao Jiang, Zhiming Zhao
Summary: This paper proposes a non-negative matrix factorization feature expansion (NMFFE) approach to overcome the feature-sparsity issue in short-text. By considering the internal relationships between short texts and words, and utilizing dimensionality reduction and feature selection methods, the NMFFE algorithm improves the accuracy of short text classification.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wen-Sheng Chen, Qianwen Zeng, Binbin Pan
Summary: This paper provides a theoretical research and analysis on Deep Nonnegative Matrix Factorization (Deep NMF) and categorizes its algorithms into five categories. The clustering performance of Deep NMF algorithms on face databases is investigated, and the design principles, application domains, and evolution of Deep NMF methods are comprehensively analyzed. Moreover, some open problems of Deep NMF are discussed.
Article
Computer Science, Artificial Intelligence
Wenhui Wu, Junhui Hou, Shiqi Wang, Sam Kwong, Yu Zhou
Summary: In this paper, we propose a semi-supervised adaptive kernel concept factorization (SAKCF) method that integrates data representation and kernel learning and solves the problem using an alternating iterative algorithm. Experimental results demonstrate the effectiveness and advantages of SAKCF over other methods in clustering tasks.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoxia Zhang, Xianjun Zhou, Lu Chen, Yanjun Liu
Summary: Explicable recommendation systems are important for improving the persuasiveness of the system and enhancing user trust. However, the presence of latent features makes it challenging to interpret recommendation results. To address this, a novel method called PE-NMF is proposed, which replaces latent variables with explicit data to help users understand the features of recommended items and make better decisions. Experimental results demonstrate that PE-NMF performs well in rating prediction and top-N recommendation, outperforming FE-NMF and maintaining comparable recommendation ability to NMF.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Behnam Asghari Beirami, Mehdi Mokhtarzade
Summary: This study proposes a new method for classifying hyperspectral images using weighted local kernel matrix features and improved binary grey wolf optimization. Experimental results show that this method outperforms some state-of-the-art spatial-spectral classification methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Liying Hu, Xian Chen, Gongde Guo, Lifei Chen
Summary: The study proposed a combined kernel nonnegative matrix factorization method, which can extract both global and local nonlinear features for small-sample face recognition, outperforming other KNMF methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Xiaoxia Zhang, Degang Chen, Hong Yu, Guoyin Wang, Houjun Tang, Kesheng Wu
Summary: Nonnegative Matrix Factorization (NMF) produces interpretable solutions for applications like collaborative filtering. Regularization is needed to address issues like overfitting and interpretability. Existing regularizers are constructed from factorization results, but this study proposes a more holistic graph regularizer based on a linear projection of the rating matrix, named LPGNMF. Experimental results show the superiority of LPGNMF on different datasets.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Duc P. Truong, Erik Skau, Derek Desantis, Boian Alexandrov
Summary: A novel approach to Boolean matrix factorization is presented, which solves a nonnegative optimization problem with an additional constraint over an auxiliary matrix. The equivalence of the two solution spaces is proved, and the nonincreasing property of the algorithm is also demonstrated. Experiments on synthetic and real datasets show the effectiveness and complexity of the algorithm compared to other methods.
Article
Computer Science, Artificial Intelligence
Junjun Pan, Nicolas Gillis
Summary: Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data, which can be efficiently computed under the separability assumption. The algorithm operates by finding data points that contain basis vectors for decomposition.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Nicolas Nadisic, Jeremy E. Cohen, Arnaud Vandaele, Nicolas Gillis
Summary: This paper introduces a new form of sparse MNNLS problem and a two-step algorithm to solve it. By dividing the problem into subproblems and selecting Pareto front solutions, a matrix that satisfies the sparsity constraint is constructed. Experimental results show that this method is more accurate than existing heuristic algorithms.
Article
Computer Science, Artificial Intelligence
Kahoko Takahashi, Zhe Sun, Jordi Sole-Casals, Andrzej Cichocki, Anh Huy Phan, Qibin Zhao, Hui-Hai Zhao, Shangkun Deng, Ruggero Micheletto
Summary: This study proposed a method of generating artificial data using empirical mode decomposition (EMD) to train neural networks for brain computer interfaces. The experiments showed that introducing artificial frames significantly improved performance and reduced the number of experiments and training costs.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Tatsuya Yokota, Hidekata Hontani, Qibin Zhao, Andrzej Cichocki
Summary: Deep image prior (DIP) is an effective method that uses deep convolutional neural networks (ConvNet) as image priors for various image restoration applications. This study proposes a interpretable approach called manifold modeling in embedded space (MMES), which provides similar results to DIP by dividing convolution into delay embedding and transformation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Tian Wang, Anastasios Bezerianos, Andrzej Cichocki, Junhua Li
Summary: A multi-kernel capsule network (MKCapsnet) was proposed for identifying schizophrenia, considering brain anatomical structure and outperforming existing methods. Comparison of performances using different parameters and illustration of routing process revealed characteristics of the proposed method.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Shu Gong, Kaibo Xing, Andrzej Cichocki, Junhua Li
Summary: This article summarizes the recent progress and future developments of deep learning in the field of electroencephalogram (EEG). It introduces the removal of artifacts and the utilization of deep learning models in EEG processing and classification. The applications of deep learning in EEG are categorized and reviewed, followed by a discussion on the pros and cons and proposed future directions and challenges. This article serves as a summary of past work and a starting point for further developments in EEG studies based on deep learning.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhiqiang Wang, Jing Jin, Ren Xu, Chang Liu, Xingyu Wang, Andrzej Cichocki
Summary: In this study, we proposed new methods based on 2DLPP and 2DLDA to address the limitations of TRCA. These methods can utilize the information between classes more efficiently and improve the performance of SSVEP target recognition.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yong Peng, Feiwei Qin, Wanzeng Kong, Yuan Ge, Feiping Nie, Andrzej Cichocki
Summary: Accurate and automatic recognition of human emotional states is a central task in affective computing. This article proposes a unified framework called GFIL that can adaptively identify the importance of different EEG features, frequency bands, and channels in emotion expression. Experiment results show that GFIL achieves improved accuracies in emotion recognition by utilizing a feature autoweighting strategy.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2022)
Article
Mathematics
Konstantin Sobolev, Dmitry Ermilov, Anh-Huy Phan, Andrzej Cichocki
Summary: This paper proposes a Proxy-based Automatic tensor Rank Selection method (PARS) to find the best combination of ranks for neural network compression. Experimental results show that PARS improves the results of existing decomposition methods on multiple neural networks.
Article
Engineering, Electrical & Electronic
Maame G. Asante-Mensah, Anh Huy Phan, Salman Ahmadi-Asl, Zaher Al Aghbari, Andrzej Cichocki
Summary: This paper presents a pixel selection method based on superpixel segmentation and tensor completion for compact image representation. The method divides the image into regions capturing important textures or semantics and selects a representative pixel from each region for storage. Experimental results show that centroid pixel performs the best among different criteria for selecting the representative pixel. Additionally, two smooth tensor completion algorithms are proposed to effectively reconstruct different types of images from the selected pixels. The experiments also demonstrate that the superpixel-based method outperforms uniform sampling for various missing ratios.
Article
Computer Science, Artificial Intelligence
Hangjun Che, Jun Wang, Andrzej Cichocki
Summary: In this article, the sparse nonnegative matrix factorization problem is formulated as a mixed-integer bicriteria optimization problem. A two-timescale duplex neurodynamic approach is used to solve the problem, achieving low error, high sparsity, and high score.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Jin, Zhiqiang Wang, Ren Xu, Chang Liu, Xingyu Wang, Andrzej Cichocki
Summary: The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely studied due to its advantages in training time, recognition performance, and information transmission rate. This article introduces a novel time filter and similarity measurement methods based on task-related component analysis (TRCA) to improve the detection ability of SSVEPs. Experimental results demonstrate that the proposed methods outperform existing methods and show promising potential for SSVEP detection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Biomedical
Runze Wu, Jing Jin, Ian Daly, Xingyu Wang, Andrzej Cichocki
Summary: This study proposes a new deep learning model for motor imagery-based brain-computer interface systems. The model utilizes a convolutional neural network with a multi-scale and channel-temporal attention module to automatically extract features and improve recognition ability.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Yong Peng, Fengzhe Jin, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki
Summary: This paper proposes an OptimalGraph coupled Semi-Supervised Learning (OGSSL) model for EEG emotion recognition, which unifies adaptive graph learning and emotion recognition into a single objective. By optimizing graph learning and improving the label indicator matrix of unlabeled samples, it achieves high accuracy in emotion recognition and automatically identifies EEG frequency bands and brain regions correlated with emotions.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Yong Peng, Yikai Zhang, Wanzeng Kong, Feiping Nie, Bao-Liang Lu, Andrzej Cichocki
Summary: This paper proposes a semi-supervised sparse low-rank regression model called (SLRR)-L-3 to unify discriminative subspace identification and semi-supervised emotion recognition. Experimental results show that the emotion recognition performance is greatly improved by the joint learning mechanism of (SLRR)-L-3, and the model exhibits additional abilities in affective activation patterns exploration and EEG feature selection.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie, Jinglong Fang, Bao-Liang Lu, Andrzej Cichocki
Summary: The proposed self-weighted semi-supervised classification model successfully addressed the issues in EEG-based cross-session emotion recognition and activation patterns mining. Experimental results showed that the Gamma frequency band is the most crucial for emotion recognition, and specific EEG channels play key roles in cross-session emotion recognition.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
Salman Ahmadi-Asl, Cesar F. Caiafa, Andrzej Cichocki, Anh Huy Phan, Toshihisa Tanaka, Ivan Oseledets, Jun Wang
Summary: This paper introduces Cross Tensor Approximation (CTA) as a tool for fast low-rank tensor approximation, discussing various generalizations and evaluating its performance through computer simulations.
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.
