Pattern Recognition最新文献

Gradient semi-masking for improving adversarial robustness 提高对抗鲁棒性的梯度半掩蔽

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-10 DOI: 10.1016/j.patcog.2025.112433

Xinlei Liu , Tao Hu , Peng Yi , Baolin Li , Jichao Xie , Hailong Ma

{"title":"Gradient semi-masking for improving adversarial robustness","authors":"Xinlei Liu , Tao Hu , Peng Yi , Baolin Li , Jichao Xie , Hailong Ma","doi":"10.1016/j.patcog.2025.112433","DOIUrl":"10.1016/j.patcog.2025.112433","url":null,"abstract":"<div><div>In gradient masking, certain complex signal processing and probabilistic optimization strategies exhibit favorable characteristics such as nonlinearity, irreversibility, and feature preservation, thereby providing new solutions for adversarial defense. Inspired by this, this paper proposes a plug-and-play <strong>gradient semi-masking module</strong> (<strong>GSeM</strong>) to improve the adversarial robustness of neural networks. GSeM primarily contains a feature straight-through pathway that allows for normal gradient propagation and a feature mapping pathway that interrupts gradient flow. The multi-pathway and semi-masking characteristics cause GSeM to exhibit opposing behaviors when processing data and gradients. Specifically, during data processing, GSeM compresses the state space of features while introducing white noise augmentation. However, during gradient processing, it leads to inefficient updates to certain parameters and ineffective generation of training examples. To address this shortcoming, we correct gradient propagation and introduce gradient-corrected adversarial training. Extensive experiments demonstrate that GSeM differs fundamentally from earlier gradient masking methods: it can genuinely enhance the adversarial defense performance of neural networks, surpassing previous state-of-the-art approaches.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112433"},"PeriodicalIF":7.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preserving privacy without compromising accuracy: Machine unlearning for handwritten text recognition 在不影响准确性的情况下保护隐私：手写文本识别的机器学习

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-09 DOI: 10.1016/j.patcog.2025.112411

Lei Kang, Xuanshuo Fu, Lluis Gomez, Alicia Fornés, Ernest Valveny, Dimosthenis Karatzas

{"title":"Preserving privacy without compromising accuracy: Machine unlearning for handwritten text recognition","authors":"Lei Kang, Xuanshuo Fu, Lluis Gomez, Alicia Fornés, Ernest Valveny, Dimosthenis Karatzas","doi":"10.1016/j.patcog.2025.112411","DOIUrl":"10.1016/j.patcog.2025.112411","url":null,"abstract":"<div><div>Handwritten Text Recognition (HTR) is crucial for document digitization, but handwritten data can contain user-identifiable features, like unique writing styles, posing privacy risks. Regulations such as the “right to be forgotten” require models to remove these sensitive traces without full retraining. We introduce a practical encoder-only transformer baseline as a robust reference for future HTR research. Building on this, we propose a two-stage unlearning framework for multihead transformer HTR models. Our method combines neural pruning with machine unlearning applied to a writer classification head, ensuring sensitive information is removed while preserving the recognition head. We also present Writer-ID Confusion (WIC), a method that forces the forget set to follow a uniform distribution over writer identities, unlearning user-specific cues while maintaining text recognition performance. We compare WIC to Random Labeling, Fisher Forgetting, Amnesiac Unlearning, and DELETE within our prune-unlearn pipeline and consistently achieve better privacy and accuracy trade-offs. This is the first systematic study of machine unlearning for HTR. Using metrics such as Accuracy, Character Error Rate (CER), Word Error Rate (WER), and Membership Inference Attacks (MIA) on the IAM and CVL datasets, we demonstrate that our method achieves state-of-the-art or superior performance for effective unlearning. These experiments show that our approach effectively safeguards privacy without compromising accuracy, opening new directions for document analysis research. Our code is publicly available at <span><span>https://github.com/leitro/WIC-WriterIDConfusion-MachineUnlearning</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112411"},"PeriodicalIF":7.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive integration of textual context and visual embeddings for underrepresented vision classification 文本上下文和视觉嵌入的自适应集成用于未充分代表的视觉分类

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-08 DOI: 10.1016/j.patcog.2025.112420

Seongyeop Kim , Hyung-Il Kim , Yong Man Ro

{"title":"Adaptive integration of textual context and visual embeddings for underrepresented vision classification","authors":"Seongyeop Kim , Hyung-Il Kim , Yong Man Ro","doi":"10.1016/j.patcog.2025.112420","DOIUrl":"10.1016/j.patcog.2025.112420","url":null,"abstract":"<div><div>The advancement of deep learning has significantly improved image classification performance; however, handling long-tail distributions remains challenging due to the limited data available for rare classes. Existing approaches predominantly focus on visual features, often neglecting the valuable contextual information provided by textual data, which can be especially beneficial for classes with sparse visual examples. In this work, we introduce a novel method addressing this limitation by integrating textual data generated by advanced language models with visual inputs through our newly proposed Adaptive Integration Block for Vision-Text Synergy (AIB-VTS). Specifically designed for Vision Transformer architectures, AIB-VTS adaptively balances visual and textual information during inference, effectively utilizing textual descriptions generated from large language models. Extensive experiments on benchmark datasets demonstrate substantial performance improvements across all class groups, particularly in underrepresented (tail) classes. These results confirm the effectiveness of our approach in leveraging textual context to mitigate data scarcity issues and enhance model robustness.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112420"},"PeriodicalIF":7.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PCFFusion: Progressive cross-modal feature fusion network for infrared and visible images PCFFusion：红外和可见光图像的渐进式跨模态特征融合网络

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-08 DOI: 10.1016/j.patcog.2025.112419

Shuying Huang , Kai Zhang , Yong Yang , Weiguo Wan

{"title":"PCFFusion: Progressive cross-modal feature fusion network for infrared and visible images","authors":"Shuying Huang , Kai Zhang , Yong Yang , Weiguo Wan","doi":"10.1016/j.patcog.2025.112419","DOIUrl":"10.1016/j.patcog.2025.112419","url":null,"abstract":"<div><div>Infrared and visible image fusion (IVIF) aims to fuse thermal target information in infrared images and spatial texture information in visible images, improving the observability and comprehensibility of the fused images. Currently, most IVIF methods suffer from the loss of salient target information and texture details in fused images. To alleviate this problem, a progressive cross-modal feature fusion network (PCFFusion) for IVIF is proposed, which comprises two stages: feature extraction and feature fusion. In the feature extraction stage, to enhance the network’s feature representation capability, a feature decomposition module (FDM) is constructed to extract two modal features of different scales by defining a feature decomposition operation (FDO). In addition, by establishing correlations between the high- frequency and low-frequency components of two modal features, a cross-modal feature enhancement module (CMFEM) is built to realize correction and enhancement of the two features at each scale. The feature fusion stage achieves the fusion of two modal features at each scale and the supplementation of adjacent scale features by constructing three cross-domain fusion module (CDFMs). To constrain the fused results preserve more salient targets and richer texture details, a dual-feature fidelity loss function is defined by constructing a salient weight map to balance the two loss terms. Extensive experiments demonstrate that fusion results of the proposed method highlight prominent targets from infrared images while retaining rich background details from visible images, and the performance of PCFFusion is superior to some advanced methods. Specifically, compared to the optimal results obtained by other comparison methods, the proposed network achieves an average increase of 30.35 % and 10.9 % in metrics Mutual Information (MI) and Standard deviation (SD) on the TNO dataset, respectively.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112419"},"PeriodicalIF":7.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MMP: Enhancing unsupervised graph anomaly detection with multi-view message passing MMP：通过多视图消息传递增强无监督图异常检测

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-07 DOI: 10.1016/j.patcog.2025.112388

Weihu Song , Lei Li , Mengxiao Zhu , Yue Pei , Haogang Zhu

{"title":"MMP: Enhancing unsupervised graph anomaly detection with multi-view message passing","authors":"Weihu Song , Lei Li , Mengxiao Zhu , Yue Pei , Haogang Zhu","doi":"10.1016/j.patcog.2025.112388","DOIUrl":"10.1016/j.patcog.2025.112388","url":null,"abstract":"<div><div>The complementary and conflicting relationships between views are two fundamental issues when applying Graph Neural Networks (GNNs) to multi-view attributed graph anomaly detection. Most existing approaches do not address the inherent multi-view properties in the attribute space or leverage complementary information through simple representation fusion, which overlooks the conflicting information among different views. In this paper, we argue that effectively applying GNNs to multi-view anomaly detection necessitates reinforcing complementary information between views and, more importantly, managing conflicting information. Building on this perspective, this paper introduces Multi-View Message Passing (MMP), a novel and effective message passing paradigm specifically designed for multi-view anomaly detection. In the multi-view aggregation phase of MMP, views containing different types of information are integrated using view-specific aggregation functions. This approach enables the model to dynamically adjust the amount of information aggregated from complementary and conflicting views, thereby mitigating issues arising from insufficient complementary information and excessive conflicting information, which can lead to suboptimal representation learning. Furthermore, we propose an innovative aggregation loss mechanism that enhances model performance by optimizing the reconstruction differences between aggregated representations and the original views, thereby improving both detection accuracy and model interpretability. Extensive experiments on synthetic and real-world datasets validate the effectiveness and robustness of our method. The source code is available at <span><span>https://github.com/weihus/MMP</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112388"},"PeriodicalIF":7.6,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning from majority label: A novel problem in multi-class multiple-instance learning 多数标签学习：多类多实例学习中的一个新问题

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-07 DOI: 10.1016/j.patcog.2025.112425

Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro, Ryoma Bise

{"title":"Learning from majority label: A novel problem in multi-class multiple-instance learning","authors":"Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro, Ryoma Bise","doi":"10.1016/j.patcog.2025.112425","DOIUrl":"10.1016/j.patcog.2025.112425","url":null,"abstract":"<div><div>The paper proposes a novel multi-class Multiple-Instance Learning (MIL) problem called Learning from Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag-level label. The goal of LML is to train a classification model that estimates the class of each instance using the majority label. This problem is valuable in a variety of applications, including pathology image segmentation, political voting prediction, customer sentiment analysis, and environmental monitoring. To solve LML, we propose a Counting Network trained to produce bag-level majority labels, estimated by counting the number of instances in each class. Furthermore, analysis experiments on the characteristics of LML revealed that bags with a high proportion of the majority class facilitate learning. Based on this result, we developed a Majority Proportion Enhancement Module (MPEM) that increases the proportion of the majority class by removing minority class instances within the bags. Experiments demonstrate the superiority of the proposed method on four datasets compared to conventional MIL methods. Moreover, ablation studies confirmed the effectiveness of each module. The code is available at <span><span>here</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112425"},"PeriodicalIF":7.6,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feature subset weighting for distance-based supervised learning 基于距离监督学习的特征子集加权

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-07 DOI: 10.1016/j.patcog.2025.112424

Adnan Theerens , Yvan Saeys , Chris Cornelis

{"title":"Feature subset weighting for distance-based supervised learning","authors":"Adnan Theerens , Yvan Saeys , Chris Cornelis","doi":"10.1016/j.patcog.2025.112424","DOIUrl":"10.1016/j.patcog.2025.112424","url":null,"abstract":"<div><div>This paper introduces feature subset weighting using monotone measures for distance-based supervised learning. The Choquet integral is used to define a distance function that incorporates these weights. This integration enables the proposed distances to effectively capture non-linear relationships and account for interactions both between conditional and decision attributes and among conditional attributes themselves, resulting in a more flexible distance measure. In particular, we show how this approach ensures that the distances remain unaffected by the addition of duplicate and strongly correlated features. Another key point of this approach is that it makes feature subset weighting computationally feasible, since only <span><math><mi>m</mi></math></span> feature subset weights should be calculated each time instead of calculating all feature subset weights (<span><math><msup><mn>2</mn><mi>m</mi></msup></math></span>), where <span><math><mi>m</mi></math></span> is the number of attributes. Next, we also examine how the use of the Choquet integral for measuring similarity leads to a non-equivalent definition of distance. The relationship between distance and similarity is further explored through dual measures. Additionally, symmetric Choquet distances and similarities are proposed, preserving the classical symmetry between similarity and distance. Finally, we introduce a concrete feature subset weighting distance, evaluate its performance in a <span><math><mi>k</mi></math></span>-nearest neighbours (KNN) classification setting, and compare it against Mahalanobis distances and weighted distance methods.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112424"},"PeriodicalIF":7.6,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyper-network curvature: A new representation method for high-order brain network analysis 超网络曲率：一种高阶脑网络分析的新表征方法

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-06 DOI: 10.1016/j.patcog.2025.112397

Kai Ma , Tianyu Du , Qi Zhu , Xuyun Wen , Jiashuang Huang , Xibei Yang , Daoqiang Zhang

{"title":"Hyper-network curvature: A new representation method for high-order brain network analysis","authors":"Kai Ma , Tianyu Du , Qi Zhu , Xuyun Wen , Jiashuang Huang , Xibei Yang , Daoqiang Zhang","doi":"10.1016/j.patcog.2025.112397","DOIUrl":"10.1016/j.patcog.2025.112397","url":null,"abstract":"<div><div>Human brain is a complex system and contains abundant high-order interactions among multiple brain regions, which can be described by brain hyper-network. In brain hyper-networks, nodes represent brain regions of interest (ROIs), while edges describe the interactions of multiple ROIs, providing important high-order information for brain disease analysis and diagnosis. However, most of the existing hyper-network studies focused on the hyper-connection (i.e. hyper-edge) analysis and ignored the local topological information on nodes. To address this problem, we propose a new representation method (i.e., hyper-network curvature) for brain hyper-network analysis. Compared with the existing hyper-network representation methods, the proposed hyper-network curvature can be used to analyze the local topologies of nodes in brain hyper-networks. Based on hyper-network curvature, we further propose a novel graph kernel called brain hyper-network curvature kernel to measure the similarity of a pair of brain hyper-networks. We have proved that the proposed hyper-network curvature is bounded and brain hyper-network curvature kernel is positive definite. To evaluate the effectiveness of our proposed method, we perform the classification experiments on functional magnetic resonance imaging data of brain diseases. The experimental results demonstrate that our proposed method can significantly improve classification accuracy compared to the state-of-the-art graph kernels and graph neural networks for classifying brain diseases.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112397"},"PeriodicalIF":7.6,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MIGF-Net: Multimodal interaction-guided fusion network for image aesthetics assessment MIGF-Net：用于图像美学评估的多模态交互引导融合网络

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-05 DOI: 10.1016/j.patcog.2025.112401

Yun Liu , Zhipeng Wen , Leida Li , Peiguang Jing , Daoxin Fan

{"title":"MIGF-Net: Multimodal interaction-guided fusion network for image aesthetics assessment","authors":"Yun Liu , Zhipeng Wen , Leida Li , Peiguang Jing , Daoxin Fan","doi":"10.1016/j.patcog.2025.112401","DOIUrl":"10.1016/j.patcog.2025.112401","url":null,"abstract":"<div><div>With the development of social media, people like to post images and comments to share their ideas, which provides rich visual and textural semantic information for image aesthetics assessment (IAA). However, most previous works either extracted the unimodal aesthetic features from image due to the difficulty of obtaining comments, or combined multimodal information together but ignoring the interactive relationship between image and comment, which limits the overall performance. To solve the above problem, we propose a Multimodal Interaction-Guided Fusion Network (MIGF-Net) for image aesthetics assessment based on both image and comment semantic information, which can not only solve the challenge of comment generating, but also provide the multimodal feature interactive information. Specifically, considering the coupling mechanism of the image theme, we construct a visual semantic fusion module to extract the visual semantic feature based on the visual attributes and the theme features. Then, a textural semantic feature extractor is designed to mine the semantic information hidden in comments, which not only addresses the issue of missing comments but also effectively complements the visual semantic features. Furthermore, we establish a Dual-Stream Interaction-Guided Fusion module to fuse the semantic features of images and comments, fully exploring the interactive relationship between images and comments in the human brain’s perception mechanism. Experimental results on two public image aesthetics evaluation datasets demonstrate that our model outperforms the current state-of-the-art methods. Our code will be released at <span><span>https://github.com/wenzhipeng123/MIGF-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112401"},"PeriodicalIF":7.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time series adaptive mode decomposition (TAMD): Method for improving forecasting accuracy in the apparel industry 时间序列自适应模式分解（TAMD）：一种提高服装行业预测精度的方法

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-05 DOI: 10.1016/j.patcog.2025.112417

Guangbao Zhou , Pengliang Liu , Quanle Lin , Miao Qian , Zhong Xiang , Zeyu Zheng , Lixian Liu

{"title":"Time series adaptive mode decomposition (TAMD): Method for improving forecasting accuracy in the apparel industry","authors":"Guangbao Zhou , Pengliang Liu , Quanle Lin , Miao Qian , Zhong Xiang , Zeyu Zheng , Lixian Liu","doi":"10.1016/j.patcog.2025.112417","DOIUrl":"10.1016/j.patcog.2025.112417","url":null,"abstract":"<div><div>Accurate forecasting of apparel sales is critical for inventory management, supply chain optimization, and market strategy planning. However, existing forecasting models often struggle to effectively capture the complex characteristics of apparel sales data, such as distinct seasonality, cyclicality, and strongly nonlinear fluctuations, which significantly hinder prediction accuracy and generalization ability. To address these challenges, this study introduces a novel Time series Adaptive Mode Decomposition (TAMD)-based forecasting algorithm. The proposed method: (1) employs Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and sample entropy-guided Variational Mode Decomposition (VMD) to separate the input time series into noise components and multiple smooth Intrinsic Mode Functions (IMFs), to better capture intrinsic data dynamics; (2) refines the sub-series distribution features via an adaptive module guided by sample entropy, dividing each sub-series into subsequences with maximal distribution difference to improve adaptability to periodic changes and market volatility; (3) predicts each subsequence with adaptive distribution matching based on discontinuous random subsequence combinations, and then linearly superposes the prediction results as a final output, thereby boosting accuracy and generalizability. Comprehensive experiments on both public and self-constructed datasets (including four years of Taobao sales data for dresses, jeans, sweatshirts, and sweaters, totaling over 44.7 million records) demonstrate that TAMD outperforms existing methods significantly, highlighting its effectiveness in revealing the complexity of apparel market data and enhancing prediction performance.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112417"},"PeriodicalIF":7.6,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0