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Late Fusion Multiview Clustering via Min-Max Optimization

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9417-9427页

作者： Li, Miaomiao Liu, Xinwang Zhang, Yi Liang, Weixuan Changsha Univ Coll Elect Informat & Elect Engn Changsha Peoples R China Natl Univ Def Technol Coll Comp Changsha Peoples R China

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k-means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms.

关键词： Clustering algorithms Optimization Kernel partitioning algorithms Perturbation methods Minimax techniques Task analysis Clustering ensemble multiple kernel clustering multiview clustering (MVC)

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Towards More Precise Coincidental Correctness Detection With Deep Semantic Learning

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第12期50卷 3265-3289页

作者： Xie, Huan Lei, Yan Yan, Meng Li, Shanshan Mao, Xiaoguang Yu, Yue Lo, David Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410003 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

Coincidental correctness (CC) is a situation during the execution of a test case, the buggy entity is executed, but the program behaves correctly as expected. Many automated fault localization (FL) techniques use runtime information to discover the underlying connection between the executed buggy entity and the failing test result. The existence of CC will weaken such connection, mislead the FL algorithms to build inaccurate models, and consequently, decrease the localization accuracy. To alleviate the adverse effect of CC on FL, CC detection techniques have been proposed to identify the possible CC tests via heuristic or machine learning algorithms. However, their performance on precision is not satisfactory since they overestimate the possible CC tests and are insufficient in learning the deep semantic features. In this work, we propose a novel Triplet network-based Coincidental Correctness detection technique (i.e., TriCoCo) to overcome the limitations of the prior works. TriCoCo narrows the possible CC tests by designing three features to identify genuine passing tests. Instead of using all tests as inputs by existing techniques, TriCoCo takes the identified genuine passing tests and failing ones to train a triplet model that can evaluate their relative distance. Finally, TriCoCo infers the probability of being a CC test of the test in the rest of the passing tests by using the trained triplet model. We conduct large-scale experiments to evaluate TriCoCo based on the widely-used Defects4J benchmark. The results demonstrate that TriCoCo can improve not only the precision of CC detection but also the effectiveness of FL techniques, e.g., the precision of TriCoCo is 80.33% on average, and TriCoCo boosts the efficacy of DStar by 18%-74% in terms of MFR metric when compared to seven state-of-the-art CC detection baselines.

关键词： Feature extraction Semantics Machine learning algorithms Location awareness Deep learning Clustering algorithms Prediction algorithms Benchmark testing Accuracy partitioning algorithms Coincidental correctness fault localization deep semantic learning

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Consensus Clustering With Co-Association Matrix Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 4192-4205页

作者： Shi, Yifan Yu, Zhiwen Chen, C. L. Philip Zeng, Huanqiang Huaqiao Univ Sch Engn Quanzhou 362021 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China

Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization;2) the information from the original dataset is rarely considered;and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.

关键词： Optimization Clustering algorithms partitioning algorithms Linear programming Correlation Clustering methods Stochastic processes Alternative optimization consensus clustering

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Adaptive Detachable Partition-Based Reference Frame Recompression for Video Coding

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IEEE MULTIMEDIA 2024年第2期31卷 17-25页

作者： Zhou, Jinjia Fu, Chen Hosei Univ English Based Grad program Tokyo 1848584 Japan Hosei Univ Sci & Engn Tokyo 1848584 Japan

To reduce the huge off-chip dynamic random-access memory bandwidth which occupies a significant portion of the system power, reference frame recompression (RFRC) becomes one of the most vital components in the application-specified integrated circuit implementation of video codecs. RFRC challenges to increase the compression ratio with low-complexity and support random access of the compressed data. This article presents an adaptive detachable partition-based RFRC scheme which is capable of compressing a variable-size to a fixed bit of access unit. Compared with the conventional schemes which compress fixed-size partition to variable bits, this work can increase the compression ratio by greatly reducing the redundancy of the partitions' boundaries. Moreover, a high-speed coding algorithm is applied to reduce the complexity and increase the parallelism of compressing and decompressing. Finally, a new memory mapping strategy is utilized to support random access. As a result, the reference frame can be compressed by 68% with no quality degradation.

关键词： Encoding partitioning algorithms Random access memory Decoding Complexity theory Termination of employment Video coding

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Can Evolutionary Clustering Have Theoretical Guarantees?

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第5期28卷 1220-1234页

作者： Qian, Chao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

Clustering is a fundamental problem in many areas, which aims to partition a given data set into groups based on some distance measure, such that the data points in the same group are similar while that in different groups are dissimilar. Due to its importance and NP-hardness, a lot of methods have been proposed, among which evolutionary algorithms (EAs) are a class of popular ones. Evolutionary clustering has found many successful applications, but all the results are empirical, lacking theoretical support. This article fills this gap by proving that the approximation performance of the global simple evolutionary multiobjective optimizer (GSEMO) (a simple multiobjective EA) for solving four formulations of clustering, i.e., k-tMM, k-center, discrete k-median, and k-means, can be theoretically guaranteed. Furthermore, we consider clustering under fairness, which tries to avoid algorithmic bias, and has recently been an important research topic in machine learning. We prove that for discrete k-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.

关键词： Clustering algorithms Linear programming partitioning algorithms Evolutionary computation Data analysis Heuristic algorithms Euclidean distance Clustering discrete k-median evolutionary algorithms (EAs) fairness k-tMM k-center k-means theoretical analysis

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A Seed Growth Algorithm for Local Clustering in Complex Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5878-5891页

作者： Tsai, Feng-Sheng Hsu, Sheng-Yi Shih, Mau-Hsiang China Med Univ Dept Biomed Informat Taichung 404328 Taiwan China Med Univ Hosp Res Ctr Interneural Comp Taichung 404327 Taiwan Ever Fortune AI Co Ltd Taichung 406040 Taiwan China Med Univ China Med Univ Hosp Res Ctr Interneural Comp Taichung 404327 Taiwan

A seed growth algorithm based on a local connectivity rule for cluster formation in complex networks is introduced. That accompanies with the cluster normalization algorithm, the parameter determination process, and the pseudocluster inference process, forming the coherent algorithms and formalizing the categories within the realm of the cluster space. The prime clusters can be extracted from the cluster space, so that the overlapping complexity of clusters is confined to the prime clusters. To decide unequivocally whether the coherent algorithms are efficient, we have to simulate on the overlapping stochastic block networks. Our simulation shows that the dice coefficient of the prime cluster corresponding to the overlapping target cluster is 0.978 +/- 0.024 on average. It decodes the underlying meaning that the coherent algorithms can efficiently search out the prime clusters containing almost the same nodes as the overlapping clusters. It provides a firm foundation for a simulation on the Caenorhabditis elegans neuronal network, unraveling the neurons DD04, PDB, VA08, VB06, VB07, VD07, and VD08 lying in the major overlap of clusters, among them the ablation of DD04 and PDB in biological experiments has shown to result in a pronounced loss of controllability of motor behavior.

关键词： Clustering algorithms Neurons partitioning algorithms Inference algorithms Complex networks Linear programming Stochastic processes Caenorhabditis elegans neuronal networks local cluster formation locomotor control cluster overlap stochastic block networks

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Graph Partition and Multiple Choice-UCB Based algorithms for Edge Server Placement in MEC Environment

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 4050-4061页

作者： Zhao, Zheyu Cheng, Hao Xu, Xiaohua Pan, Yi Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China Delft Univ Technol Dept Elect Engn Math & Comp Sci NL-2628 CD Delft Netherlands Chinese Acad Sci Shenzhen Inst Adv Technol Fac Comp Sci & Control Engn Beijing 100045 Peoples R China

The deployment of edge servers make a significant impact on the service quality of a Mobile Edge Computing (MEC) system. This service quality relies on solving two key sub-problems: 1) interference management between servers 2) the placement of MEC servers. To improve the Quality of Service (QoS), we propose a method based on Graph Partition (GP) and Upper Confidence Bound (UCB) for solving these two sub-problems. Regarding interference management, we use an undirected graph to represent the interference between MEC servers so that the overall graph can be divided into multiple subsets of non-interfering MEC servers. Regarding server placement, we propose a Multiple Choice-Upper Confidence Bound (MC-UCB) algorithm that place an collection of interference aware edge servers in each selection. To evaluate the performance, we define a user's QoS function based on transmission delay, throughput, and user density comprehensively and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) from previous work. The simulation results show that the performance of the proposed algorithms is improved by more than 4% compared with the GA algorithm and 6% compared with the PSO algorithm.

关键词： Servers Interference partitioning algorithms Delays Quality of service Throughput Simulation Mobile edge computing multiple choice-upper confidence bound graph partition edge server placement interference management

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An Improved Density-Based Clustering Algorithm Based on Cellular Neural Networks

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IEEE ACCESS 2024年 12卷 138027-138046页

作者： Xu, Jiexi Ling, Bingo Wing-Kuen Liu, Qing Wei, Yiting Zhang, Zhanbin Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

Clustering plays a major role in various disciplines including biological visual image classification. Various clustering algorithms have been introduced and developed. Among them, the density-based clustering algorithm achieves the best performance. The cellular neural network (CNN) based clustering algorithm (CNNCA) is a density-based method. It performs the clustering at a very high speed because the CNN can be implemented on a silicon chip. Nevertheless, the performance of the CNNCA is significantly degraded when facing clusters of complex structures, especially with significant density differences between clusters. In this paper, the local rules of the CNNs are modified. Moreover, the multi-spatial resolutions are employed. In particular, the values of minimum spatial resolution and the other parameters are adaptively designed. Hence, the iterative procedures can be applied to the remaining unassigned points. Furthermore, the conditional minimum distance rule is employed to assign the dispersed elements to the existing clusters. Different datasets have been utilized to evaluate the performance of our proposed method. Compared to the state-of-the-art clustering algorithms, the computer numerical simulation results show that our proposed method outperforms the existing state-of-the-art methods in higher accuracy and more robustness.

关键词： Clustering algorithms Classification algorithms Convolutional neural networks Spatial resolution partitioning algorithms Noise measurement Cellular neural networks Unsupervised learning Clustering multi-spatial resolutions cellular neural networks density-based clustering algorithm unsupervised learning

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K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11077-11090页

作者： Cheng, Dongdong Huang, Jinlong Zhang, Sulan Xia, Shuyin Wang, Guoyin Xie, Jiang Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Yangtze Normal Univ Coll Big Data & Intelligent Engn Chongqing 408100 Peoples R China

Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed to accelerate it and improve the quality of initialize cluster centers, but few researchers pay attention to the shortcoming of K-means in discovering arbitrary-shaped clusters. Using graph distance (GD) to measure the dissimilarity between objects is a good way to solve this problem, but computing the GD is time-consuming. Inspired by the idea that granular ball uses a ball to represent the local data, we select representatives from a local neighborhood, called natural density peaks (NDPs). On the basis of NDPs, we propose a novel K-means algorithm for identifying arbitrary-shaped clusters, called NDP-Kmeans. It defines neighbor-based distance between NDPs and takes advantage of the neighbor-based distance to compute the GD between NDPs. Afterward, an improved K-means with high-quality initial centers and GD is used to cluster NDPs. Finally, each remaining object is assigned according to its representative. The experimental results show that our algorithms can not only recognize spherical clusters but also manifold clusters. Therefore, NDP-Kmeans has more advantages in detecting arbitrary-shaped clusters than other excellent algorithms.

关键词： Clustering algorithms Manifolds Kernel Approximation algorithms Object recognition Resource management partitioning algorithms Granular ball graph distance (GD) K-means natural density peaks (NDPs)

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Robust PBFD Nonlinear Filtering Scheme for Active Noise Cancellation With Remote Acoustic Sensing

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IEEE ACCESS 2024年 12卷 123476-123488页

作者： Li, Yongde He, Hongming Liu, Yao Cheng, Baodong Song, Shangrui Zhu, Pengji China Tobacco Sichuan Ind Co Ltd Shifang Cigarette Factory Deyang 618499 Peoples R China

Nonlinear active noise cancellation/control (NANC) systems have attracted attention for attenuating noises using remote acoustic sensing techniques. Controlling noises at the remote virtual loaction involve higher computational requirements. In this paper, a novel robust partitioned-block frequency-domain (PBFD) nonlinear filtering scheme is proposed by combining the half-quadratic criterion (HQC) and the fast Fourier transform (FFT) method, which is called the partitioned-block frequency-domain functional link network based on HQC (PBFDFLN-HQC) algorithm. The PBFD approach for the filtering and adaptation operations can reduce the latency and computational load. A PBFDFLN-HQC-based remote virtual NANC frame has also been developed to form the partitioned-block frequency-domain filtered-s HQC (PBFDFsHQC) algorithm. Moreover, the convergence property and complexity of the proposed algorithm are analyzed. Numerical experiments verify the superiority of the proposed algorithm with computational efficiency and robustness to impulsive interferences. Realistically attenuating noises are carried out to exhibit controlling effects of the proposed algorithm for factory noises in remote virtual NANC systems.

关键词： Microphones Frequency-domain analysis Sensors Acoustics partitioning algorithms Adaptation models Noise cancellation Computational efficiency Acoustic sensors nonlinear active noise cancellation partitioned-block frequency-domain remote acoustic sensing

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