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Representation Auto-fused NMF based Hierarchical Clustering

Expert Systems with Applications 2025年 283卷

作者： Lin, Yunxia Hu, Haoyu Li, Bentian Zhao, Shuchang Jing, Hengchang School of Information Engineering Yangzhou University Yangzhou225127 China Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou225127 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Institute of Intelligent Information Processing Taizhou University Taizhou318000 China Taizhou University Taizhou318000 China

Nonnegative matrix factorization (NMF) as a powerful representation learning technique has been extensively applied in clustering tasks across diverse domains, leading to the proliferation of various NMF-based clustering methods. However, most of these methods suffer from the suboptimal performance arising from the disjointed process of representation learning and clustering, and the necessity to manually predetermine the optimal number of clusters which is a critical factor affecting the quality of clustering results. To fill this gap and inspired by convex clustering, we propose a simple yet effective NMF-based clustering method named Representation Auto-fused NMF based Hierarchical Clustering (RAFNMF-HC) by incorporating the l2 norm penalty on the differences between pairs of representations into the NMF framework to foster the automatic fusion of representations during representation learning. As the fusion continuously proceeds, similar representations gradually coalesce into small clusters, which subsequently merge into larger ones until a largest cluster encompassing all representations is formed, enabling a joint representation learning and clustering paradigm. Moreover, a cluster hierarchy as a byproduct adaptively emerges without requiring a predefined number of clusters, thus providing us with interpretability of cluster structure of data to some degree. Additionally, RAFNMF-HC only incorporates a single hyperparameter, significantly simplifying the adjustment process and boosting its practical efficiency. Our proposed RAFNMF-HC method also boasts straightforward scalability to diverse NMF variants, enhancing its versatility. Extensive experiments on various real-world datasets demonstrate the superiority of RAFNMF-HC. © 2025 Elsevier Ltd

关键词： Hierarchical clustering

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Iterative cross-training: An algorithm for web page categorization

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Intelligent Data Analysis 2003年第3期7卷 233-253页

作者： Soonthornphisaj, Nuanwan Kijsirikul, Boonserm Machine Intelligence and Knowledge Discovery Laboratory Department of Computer Engineering Chulalongkorn University Bangkok 10330 Thailand not available Bangkruay-Sainoi Rd. Bangbuathong Nonthaburi 11110 Thailand

The goal of Web page categorization is to classify Web documents into a certain number of predefined categories. Previous works in this area employed a large number of labeled training documents for supervised learning. The problem is that, it is difficult to create labeled training documents. Though it is not so easy to manually categorize unlabeled documents for creating training data, it is easy to collect unlabeled ones. Therefore, a new machine learning algorithm is investigated to overcome these difficulties and effectively utilize unlabeled documents. We propose a novel approach called Iterative Cross-Training (ICT). In this paper, we applied the algorithm to Web page categorization on three data sets. The performance of ICT was evaluated and analyzed with the supervised learning algorithms, Co-Training and Expectation Maximization. We found that ICT is considered to be an effective approach for the Web page categorization task. © 2003-IOS Press.

关键词： Websites

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Image-point cloud registration method based on image matching 3

Image-point cloud registration method based on image matchin...

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3rd International Conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Ai, Yang Yang, Xi School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China Key Laboratory of Ancient Chinese Script Culture Relics and Artificial Intelligence Jilin University China

ISBN: (纸本)9781510681880

Image-to-point cloud registration is the task of fusing 2D images with 3D point clouds, typically combining the rich texture information from images with the spatial information from point clouds for a more comprehensive scene representation. However, the feature discrepancy between different modalities often makes it challenging to accurately match. Addressing this issue, this paper proposes an image-matching-based algorithm for image-to-point cloud registration. Initially, for cross-modal issues, we use 3D visualization tools to screenshot the 3D models, reading the camera information of the screenshots to establish a correspondence between the 3D models and 2D images. By employing a pretrained image matching model, the algorithm extracts keypoints and matches images of the screenshots and images, establishing image keypoints pairing relationships. Then, using the camera projection model, it maps the 3D coordinates to image keypoints, obtaining image-to-point cloud keypoint pairing relationships and calculating the transformation matrix between the image and 3D point cloud using PnP-RANSAC. The proposed method, compared with the manual annotations method, proves to significantly improve efficiency and stability in real-world scenarios. © 2024 SPIE.

关键词： Image matching

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DIPR: Efficient Point Cloud Registration via Dynamic Iteration

arXiv

引用

arXiv 2023年

作者： Ai, Yang Bai, Qiang Li, Jindong Yang, Xi School of Artificial Intelligence Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China Key Laboratory of Ancient Chinese Script Culture Relics and Artificial Intelligence Jilin University China

Point cloud registration (PCR) is an essential task in 3D vision. Existing methods achieve increasingly higher accuracy. However, a large proportion of non-overlapping points in point cloud registration consume a lot of computational resources while negatively affecting registration accuracy. To overcome this challenge, we introduce a novel Efficient Point Cloud Registration via Dynamic Iteration framework, DIPR, that makes the neural network interactively focus on overlapping points based on sparser input points. We design global and local registration stages to achieve efficient course-to-fine processing. Beyond basic matching modules, we propose the Refined Nodes to narrow down the scope of overlapping points by using adopted density-based clustering to significantly reduce the computation amount. And our SC Classifier serves as an early-exit mechanism to terminate the registration process in time according to matching accuracy. Extensive experiments on multiple datasets show that our proposed approach achieves superior registration accuracy while significantly reducing computational time and GPU memory consumption compared to state-of-the-art methods. © 2023, CC BY-NC-SA.

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Quaternion factorization machines: A lightweight solution to intricate feature interaction modelling

arXiv

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arXiv 2021年

作者： Chen, Tong Yin, Hongzhi Zhang, Xiangliang Huang, Zi Wang, Yang Wang, Meng The School of Information Technology and Electrical Engineering The University of Queensland Australia The Machine Intelligence and Knowledge Engineering Laboratory King Abdullah University of Science and Technology Saudi Arabia The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained IoT and edge devices. In this paper, we move beyond the traditional real space where most deep FM-based models are defined, and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions, but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes' parameter size reduction in comparison to state-of-the-art peer methods. Copyright © 2021, The Authors. All

关键词： Large dataset

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Uniting heterogeneity, inductiveness, and efficiency for graph representation learning

arXiv

引用

arXiv 2021年

作者： Chen, Tong Yin, Hongzhi Ren, Jie Huang, Zi Zhang, Xiangliang Wang, Hao The School of Information Technology and Electrical Engineering The University of Queensland Australia The Machine Intelligence and Knowledge Engineering Laboratory King Abdullah University of Science and Technology Saudi Arabia Alibaba AI Labs China

With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. Also, despite the necessity of inductively producing representations for completely new nodes (e.g., in streaming scenarios), few heterogeneous GNNs can bypass the transductive learning scheme where all nodes must be known during training. Furthermore, the training efficiency of most heterogeneous GNNs has been hindered by their sophisticated designs for extracting the semantics associated with each meta path or relation. In this paper, we propose wide and deep message passing network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning. In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes. To further improve the training efficiency, we innovatively present an active downsampling strategy that drops unimportant neighbor nodes to facilitate faster information propagation. Experiments on three real-world heterogeneous graphs have further validated the efficacy of WIDEN on both transductive and inductive node representation learning, as well as the superior training efficiency against state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

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Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

Adversarial Driving Behavior Generation Incorporating Human ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhen Liu Hang Gao Hao Ma Shuo Cai Yunfeng Hu Ting Qu Hong Chen Xun Gong School of Artificial Intelligence Jilin University China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China College of Communication Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China School of Electronic and Information Engineering Tongji University Shanghai China

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel framework for generating adversarial driving behavior of background vehicle interfering against the AV to expose effective and rational risky events. Specifically, the adversarial behavior is learned by a reinforcement learning (RL) approach incorporated with the cumulative prospect theory (CPT) which allows representation of human risk cognition. Then, the extended version of deep deterministic policy gradient (DDPG) technique is proposed for training the adversarial policy while ensuring training stability as the CPT action-value function is leveraged. A comparative case study regarding the cut-in scenario is conducted on a high fidelity Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial effectiveness to infer the weakness of the tested AV.

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On the generalization of stochastic gradient descent with momentum

The Journal of Machine Learning Research

引用

The Journal of machine Learning Research 2024年第1期25卷 1132-1187页

作者： Ali Ramezani-Kebrya Kimon Antonakopoulos Volkan Cevher Ashish Khisti Ben Liang Department of Informatics University of Oslo and Visual Intelligence Centre Integreat Norwegian Centre for Knowledge-driven Machine Learning Oslo Norway Laboratory for Information and Inference Systems (LIONS) EPFL EPFL STI IEL LIONS Lausanne Switzerland Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with standard heavy-ball momentum (SGDM) becomes unbounded. Then, for smooth Lipschitz loss functions, we analyze a profiles momentum-based update rule, i.e., SGD with early momentum (SGDEM) under a broad range of step-sizes, and show that it can train machine learning models for multiple epochs with a guarantee for generalization. Finally, for the special case of strongly convex loss functions, we find a range of momentum such that multiple epochs of standard SGDM, as a special form of SGDEM, also generalizes. Extending our results on generalization, we also develop an upper bound on the expected true risk, in terms of the number of training steps, sample size, and momentum. Our experimental evaluations verify the consistency between the numerical results and our theoretical bounds. SGDEM improves the generalization error of SGDM when training ResNet-18 on ImageNet in practical distributed settings.

关键词： uniform stability generalization error heavy-ball momentum stochastic gradient descent non-convex

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PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments

arXiv

引用

arXiv 2023年

作者： Zhou, Rixin Xia, Ding Zhang, Yi Pang, Honglin Yang, Xi Li, Chuntao School of Artificial Intelligence Jilin University China The University of Tokyo Japan National University of Defense Technology College of Electrionic Engineering China School of Archaeology Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China Key Laboratory of Ancient Chinese Script Culture Relics and Artificial Intelligence Jilin Province China

In this paper, we propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem. Existing works use rule-based methods to match similar contour shapes or textures, which are always difficult to tune hyperparameters for extensive data and computationally time-consuming. Therefore, we propose a neural network that can effectively utilize neighbor textures with contour shape information to fundamentally improve performance. First, we employ a graph-based network to extract the local contour and texture features of fragments. Then, for the pair-searching task, we adopt a linear transformer-based module to integrate these local features and use contrastive loss to encode the global features of each fragment. For the pair-matching task, we design a weighted fusion module to dynamically fuse extracted local contour and texture features, and formulate a similarity matrix for each pair of fragments to calculate the matching score and infer the adjacent segment of contours. To faithfully evaluate our proposed network, we collect a real dataset and generate a simulated image fragment dataset through an algorithm we designed that tears complete images into irregular fragments. The experimental results show that our proposed network achieves excellent pair-searching accuracy, reduces matching errors, and significantly reduces computational time. Source codes and data are available at here. © 2023, CC BY.

关键词： Contourlet transform

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Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation

arXiv

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arXiv 2024年

作者： Li, Yongguang Cao, Yueqi Li, Jindong Wang, Qi Wang, Shengsheng College of Software Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education Jilin University Changchun130012 China

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model’s performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most existing SF-UDA approaches assume the availability of abundant source domain samples, which is often impractical due to the high cost of data annotation. To address the dual challenges of limited source data and privacy concerns, we introduce a data-efficient, CLIP-powered dual-branch network (CDBN). This architecture consists of a cross-domain feature transfer branch and a target-specific feature learning branch, leveraging high-confidence target domain samples to transfer text features of source domain categories while learning target-specific soft prompts. By fusing the outputs of both branches, our approach not only effectively transfers source domain category semantic information to the target domain but also reduces the negative impacts of noise and domain gaps during target training. Furthermore, we propose an unsupervised optimization strategy driven by accurate classification and diversity, preserving the classification capability learned from the source domain while generating more confident and diverse predictions in the target domain. CDBN achieves near state-of-the-art performance with far fewer source domain samples than existing methods across 31 transfer tasks on seven datasets. © 2024, CC BY.

关键词： Adversarial machine learning

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