Article
Computer Science, Artificial Intelligence
Ruizhi Zhou, Lingfeng Niu, Hong Yang
Summary: The paper introduces a new unsupervised feature selection method for attributed graphs based on regularized sparse learning, utilizing pseudo class labels to learn the interdependency between link and content information, with a new regularization term designed for learning link information.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Rui Zhang, Xuelong Li
Summary: This article proposes a novel unsupervised feature selection method by efficiently utilizing sparse fuzzy membership and introducing adaptive loss regularization with alternating l(2,1) and Frobenius regularizations to achieve a sparse and nontrivial projection matrix. The fuzzy k-means problem is embedded within the adaptive loss regression model to utilize the fuzzy cluster structure for efficient feature selection. By reformulating the problem into a general quadratic problem with an l(1) ball constraint, and solving the corresponding dual problem with closed form solutions, the effectiveness of the proposed feature selection approach is demonstrated through empirical results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Cheng Liang, Lianzhi Wang, Li Liu, Huaxiang Zhang, Fei Guo
Summary: Multi-view unsupervised feature selection has attracted attention for its efficiency and interpretability. Existing methods rely on similarity matrices for feature selection but are hindered by noise and ignore inter-view correlations. To address these issues, we propose a robust framework that leverages tensor robust principal component analysis to obtain noise-free view-specific similarity matrices. We also learn a high-quality consensus similarity matrix and impose a rank constraint for reliable pseudo cluster indicators.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Ronghua Shang, Lujuan Wang, Fanhua Shang, Licheng Jiao, Yangyang Li
Summary: The DSLRL algorithm leverages internal association information in data space and feature space for guiding feature selection. In the absence of label information, it optimizes a low-dimensional latent representation matrix of data space to provide clustering indicators, and uses non-negative and orthogonal conditions to constrain the sparse transform matrix for more accurate feature evaluation.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Mengshi Huang, Hongmei Chen, Yong Mi, Chuan Luo, Shi-Jinn Horng, Tianrui Li
Summary: In this paper, a minimum-redundant unsupervised feature selection (UFS) approach, called SLRDR, is proposed to address the problems by combining sparse latent representation learning and dual manifold regularization. The proposed approach learns a subspace of latent representation and pseudo-label matrix in the high-quality latent space, and utilizes manifold learning and sparse regression to select more discriminative features. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Guoping Kong, Yingcang Ma, Zhiwei Xing, Xiaolong Xin
Summary: In this paper, an unsupervised feature selection method based on redundancy learning and sparse regression (RSUFS) is proposed to select more representative features. The model uses l2,1-norm regression as the loss function to learn the feature weight matrix and introduces l2,0-norm constraint to obtain accurate top k features. The algorithm also considers the cosine similarity between features to reduce redundancy and selects more valuable features. Experimental results show the effectiveness of the proposed algorithm compared to benchmark datasets and other unsupervised feature selection algorithms.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jian-Sheng Wu, Jing-Xin Liu, Jun-Yun Wu, Wei Huang
Summary: In this paper, a novel unsupervised feature selection framework called dictionary learning for unsupervised feature selection via dual sparse regression is proposed. This model combines dictionary learning with sparse regression to learn an over-complete dictionary with sparse representations, eliminating redundancy and noise in the data. The significance of features is evaluated using the other sparse regression. An efficient algorithm is provided to solve this problem, and its convergence and computational complexity, which is proportional to the data dimensionality, are theoretically analyzed. The evaluation results on 9 benchmark datasets using the k-means task demonstrate the superiority of this approach in terms of effectiveness and efficiency.
APPLIED INTELLIGENCE
(2023)
Article
Automation & Control Systems
Xinyu Li, Hui Fan, Jinglei Liu
Summary: This paper proposes a one-step unsupervised clustering method based on information theoretic metric and adaptive neighbor manifold regularization method (ITMNMR). The clustering results are directly obtained according to the constructed similarity graph, avoiding extra discretization procedures. It introduces a maximum entropy regularization term into the probabilistic model to avoid trivial similarity distributions, and uses Laplacian rank constraint and l(0)-norm to construct adaptive neighbors with sparsity and strength segmentation capabilities. It also overcomes the non-Gaussian noise by introducing reconstruction based on correntropy and performs graph regularization based on clean data.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Fan Li, Shaoquan Zhang, Bingkun Liang, Chengzhi Deng, Chenguang Xu, Shengqian Wang
Summary: The spectral-spatial low-rank sparse unmixing (SSLRSU) algorithm improves unmixing accuracy through double weighting factors and low-rank regularization, showing superior performance compared to other advanced sparse unmixing strategies.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Artificial Intelligence
Hyunki Lim, Dae-Won Kim
Summary: This study introduces a new unsupervised feature selection method called DUFS, which selects a small feature set by considering pairwise dependence of features and eliminating redundant features. Experimental results show that the proposed method outperforms existing unsupervised feature selection methods in most cases.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Rong Wang, Jintang Bian, Feiping Nie, Xuelong Li
Summary: Feature selection is crucial for dealing with high-dimensional data in machine learning and data mining tasks. However, most existing methods overlook the fuzziness in the data, resulting in sub-optimal results. To address this, we propose a novel unsupervised feature selection method that simultaneously conducts fuzziness learning and sparse learning, selecting discriminative features.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Biochemical Research Methods
Paul Scherer, Maja Trebacz, Nikola Simidjievski, Ramon Vinas, Zohreh Shams, Helena Andres Terre, Mateja Jamnik, Pietro Lio
Summary: Gene expression data is often high dimensional, noisy, and has a low number of samples, making it challenging for learning algorithms. In this article, a method called Gene Interaction Network Constrained Construction (GINCCo) is proposed to construct computational graph models for gene expression data by incorporating the structure of gene interaction networks. The results of a case study on cancer phenotype prediction tasks show that GINCCo outperforms other models while greatly reducing model complexity.
Article
Computer Science, Artificial Intelligence
Rui Zhang, Yunxing Zhang, Xuelong Li
Summary: This study proposes a novel unsupervised feature selection method by exploiting embedded graph learning and constraint to select uncorrelated yet discriminative features. The adaptive graph learning method incorporates the structure of the similarity matrix into the optimization process, resulting in excellent structural performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Pei Huang, Xiaowei Yang
Summary: Unsupervised feature selection is an important topic in the fields of machine learning, pattern recognition and data mining. A novel method called AGDS is proposed to address the issues in feature selection by utilizing adaptive graph and dependency score.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Jian-Sheng Wu, Meng-Xiao Song, Weidong Min, Jian-Huang Lai, Wei-Shi Zheng
Summary: The study introduces a novel unsupervised feature selection framework JAMEL, which aims to preserve the manifold structure among data by iteratively and adaptively learning lower-dimensional embeddings. The results show the effectiveness and efficiency of the approach in various tasks such as k-means, spectral clustering and nearest neighbor classification.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)