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Center matching scheme for K-means cluster ensembles

Center matching scheme for K-means cluster ensembles

MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image Processing and Pattern Recognition

作者： Zhang, Li Zhou, Weida Wu, Caili Huo, Jieting Zou, Haishuang Jiao, Licheng Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding Xidian University 710071 China

ISBN: (纸本)9780819478078

In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select a cluster center sequence as a reference one, and then we rearrange the other cluster center sequences according to the reference sequence. Then we label the data using these matched cluster center sequences. Hence we get multiple partitions or clusterings. Finally, multiple clusterings are combined to the best labeling by using combination rules, such as the majority voting rule, the weighted voting rule and the selective weighted voting rule. Experimental results on 7 UCI data sets show that our ensemble methods could improve the clustering results effectively. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Clustering algorithms

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CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

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Journal of Electronics(China) 2010年第4期27卷 474-479页

作者： Gou Shuiping Feng Jing Jiao Licheng Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi＇an 710071 China

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

关键词： Projection Pursuit (PP) Immune Clonal Selection Algorithm (ICSA) Genetic Algorithm (GA) K-means clustering

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Adaptive multifactorial particle swarm optimisation

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CAAI Transactions on Intelligence Technology 2019年第1期4卷 37-46页

作者： Zedong Tang Maoguo Gong School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityNo.2 South TaiBai RoadXi’an 710071People’s Republic of China

Existing multifactorial particle swarm optimisation(MFPSO)algorithms only explore a relatively narrow area between the inter-task ***,these algorithms use a fixed inter-task learning probability throughout the evolution ***,the parameter is problem dependent and can be various at different stages of the *** this work,the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in *** inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task *** this mean,the particles can explore a broad search space when utilising the additional searching experiences of other *** addition,to enhance the performance on problems with different complementarity,they design a self-adaption strategy to adjust the inter-task learning probability according to the performance *** compared the proposed algorithm with the state-of-the-art algorithms on various benchmark *** results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.

关键词： MFPSO multifactorial particle swarm optimisation

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Fast non-local Lee filter for SAR image despeckling using directional projection

Fast non-local Lee filter for SAR image despeckling using di...

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6th International Conference on Radar, RADAR 2011

作者： Hua, Zhong Lu, Lu Jiao, Lc Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an 710071 China

ISBN: (纸本)9781424484416

This paper combines the Lee filter with the non-local mean filter, and a new similarity measure is derived based on the statistics of speckle noise, which extended the non-local means from the additive noise to the multiplicative noise. Also directional projection is adopted to accelerate the process of similarity computation. The results show its effectiveness. Experimental results illustrate that the proposed method can effectively smooth the speckle while preserving the details with reduced complexity. © 2011 IEEE.

关键词： Synthetic aperture radar

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image denoising with non-local means in the shearlet domain

Image denoising with non-local means in the shearlet domain

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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Zhang, Xiaohua Zhang, Qiang Jiao, L.C. Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424494040

In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach the generalized Gaussian distribution. We use the principal component analysis (PCA) for every similarity window of shearlet coefficients. Then we use Generalized Gaussian model of non-local means method to handle the shearlet coefficients. Finally, we reconstruct image with the new shearlet coefficients to obtain the result. Numerical results show that our algorithm competes favorably with nonlocal means algorithms in the case of high noise. ©2011 IEEE.

关键词： Principal component analysis

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Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi- Supervised Learning Framework

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China Communications 2020年第10期17卷 157-169页

作者： Huaji Zhou Licheng Jiao Shilian Zheng Lifeng Yang Weiguo Shen Xiaoniu Yang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi'an 710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China

Generative adversarial network(GAN)has achieved great success in many fields such as computer vision,speech processing,and natural language processing,because of its powerful capabilities for generating realistic *** this paper,we introduce GAN into the field of electromagnetic signal classification(ESC).ESC plays an important role in both military and civilian ***,in many specific scenarios,we can’t obtain enough labeled data,which cause failure of deep learning methods because they are easy to fall into ***,semi-supervised learning(SSL)can leverage the large amount of unlabeled data to enhance the classification performance of classifiers,especially in scenarios with limited amount of labeled *** present an SSL framework by incorporating GAN,which can directly process the raw in-phase and quadrature(IQ)signal *** to the characteristics of the electromagnetic signal,we propose a weighted loss function,leading to an effective classifier to realize the end-to-end classification of the electromagnetic *** validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System(ACARS)signal *** experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.

关键词： generative adversarial network semi-supervised learning electromagnetic signal classification end-to-end classification weighted loss function

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Learning of sparse fuzzy cognitive maps using evolutionary algorithm with lasso initialization 11th

Learning of sparse fuzzy cognitive maps using evolutionary a...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Wu, Kai Liu, Jing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9783319687582

Fuzzy cognitive maps (FCMs), characterized by a great deal of abstraction, flexibility, adaptability, and fuzzy reasoning, are widely used tools for modeling dynamic systems and decision support systems. Research on the problem of finding sparse FCMs from observed data is outstanding. Evolutionary algorithms (EAs) play a key role in learning FCMs from time series without expert knowledge. In this paper, we first involve sparsity penalty in the objective function optimized by EAs. To improve the performance of EAs, we develop an effective initialization operator based on the Lasso, a convex optimization approach. Comparative experiments on synthetic data with varying sizes and densities compared with other state-of-the-art methods demonstrate the effectiveness of the proposed approach. Moreover, the proposed initialization operator is able to promote to performance of EAs in learning sparse FCMs from time series. © Springer International Publishing AG 2017.

关键词： Fuzzy Cognitive Maps

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Fast-Slow-Thinking: Complex Task Solving with Large Language Models

arXiv

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arXiv 2025年

作者： Sun, Yiliu Zhang, Yanfang Zhao, Zicheng Wan, Sheng Tao, Dacheng Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China The University of Sydney Australia

Nowadays, Large Language Models (LLMs) have been gradually employed to solve complex tasks. To face the challenge, task decomposition has become an effective way, which proposes to divide a complex task into multiple simpler subtasks and then solve them separately so that the difficulty of the original task can be reduced. However, the performance of existing task decomposition methods can be suboptimal when the task contains overly complex logic and constraints. In this situation, the solution generated by LLMs may deviate from the original purpose of the task, or contain redundant or even erroneous content. Therefore, inspired by the fact that humans possess two thinking systems including fast thinking and slow thinking, this paper introduces a new task decomposition method termed "Fast-Slow-Thinking" (FST), which stimulates LLMs to solve tasks through the cooperation of Fast Thinking (FT) and Slow Thinking (ST) steps. Here FT focuses more on the general and concise aspect of the task, and ST focuses more on the details of the task. In FT, LLMs are prompted to remove the constraints of the original task, therefore simplifying it to a general and concise one. In ST, we recall the constraints removed in FT, so that LLMs can improve the answer generated in FT to meet the requirements of the original task. Therefore, our FST method enables LLMs to consider a complex problem via a human-like cognition process from coarse to fine, the effectiveness of which has been well demonstrated by the experiments on three types of tasks. Copyright © 2025, The Authors. All rights reserved.

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Emergency logistics scheduling in disaster relief based on a multi-agent genetic algorithm

Emergency logistics scheduling in disaster relief based on a...

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2016 IEEE Congress on Evolutionary Computation, CEC 2016

作者： Gan, Xiaohui Liu, Jing Hao, Xingxing Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9781509006229

The disaster emergency relief plays a vital role in reducing casualties and economic losses. Emergency logistics scheduling (ELS) aims at dispatching emergency resources to the victims of disasters, which is an important event of disaster relief. In this paper, a model for ELS in disaster relief is built that includes several suppliers with a variety of resources, several kinds of vehicles, and multiple disasters. To ensure timely and effective dispatching, an objective is first designed to minimize the completion time of scheduling and the total unsatisfied time of the whole relief. Then, a multi-agent genetic algorithm (MAGA) is proposed to solve this problem. Four evolutionary operators, namely, the crossover, mutation, self-learning and the effective neighborhood competition operators are designed for agents. The proposed algorithm is applied to the case of Chi-Chi earthquake in Taiwan, and the experimental results show that the performance of this algorithm is better than that of genetic algorithm and memetic algorithm. © 2016 IEEE.

关键词： Multi agent systems

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Multi-objective evolutionary algorithm for enhancing the robustness of networks 11th

Multi-objective evolutionary algorithm for enhancing the rob...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Li, Zheng Wang, Shanfeng Ma, Wenping Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xian710071 China

ISBN: (纸本)9789811036132

Networks can represent many real-world complex systems. Systems like internet, power grids and fuel distribution networks need to be robust and capable of surviving from failures or intentional attacks. In recent years, the measurements node-robustness and link-robustness have attracted many researchers, and some researchers use different methods to enhance one of them or both of them. In this paper, we put forward a new method which is to use a multi-objective evolutionary algorithm to enhance both these two kinds of robustness of networks against attacks. We define two objective functions which represent noderobustness and link-robustness respectively. Experiments show that our algorithm can find a good balance between improving node-robustness and link-robustness. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Complex networks

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