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Late Fusion Multiple Kernel Clustering With Local Kernel Alignment Maximization

IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 993-1007页

作者： Zhang, Tiejian Liu, Xinwang Gong, Lei Wang, Siwei Niu, Xin Shen, Li Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

Multi-view clustering, which appropriately integrates information from multiple sources to reveal data's inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest $k$ neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the nearest neighbor indicator matrix can be used to generate local structure matrix of each sample. The local kernels of each view may then be generated using the local structure matrix, retaining just the highly confident local similarities for learning the intrinsic global manifold of data. They can also be utilised to keep the block diagonal structure and improve the robustness of the underlying kernels against *** input the local kernels of each view into the kernel $k$-means (KKM) algorithm and get the local base partitions. Finally, we use a three-step iterative optimization approach to maximize the alignment of the consensus partition using base partitions and a regularisation term. As demonstrated, a significant number of trials on 11 multi-kernel benchmark datasets have shown that the proposed LF-MKC-LKA is effective and efficient. A number of experiments are also designed to demonstrate the fast convergence, excellent performance, robustness and low parameter sensitivity of the algorithm. Our code can be find at https://***/TiejianZhang/TMM21-LF-MKC-LKA.

关键词： Kernel Clustering algorithms Robustness partitioning algorithms Optimization Manifolds Benchmark testing Multiple kernel clustering neighbor local kernel local base partition block diagonal structure

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Gene Expression Data Analysis Using Feature Weighted Robust Fuzzy -Means Clustering

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2023年第1期22卷 99-105页

作者： Singh, Vikas Verma, Nishchal K. K. IIT Dept Elect Engn Kanpur 208016 India

Clustering of gene expression data has been proven to be very useful in various applications, i.e., identifying the natural structure inherent in gene expression, understanding gene functions, mining relevant information from noisy data, and understanding gene regulation. In all these applications, genes, i.e., features, play a crucial role in characterizing them into different groups. These features may be relevant, irrelevant, or redundant, but they have different contributions during the clustering process. This paper presents a novel approach by considering the effect of features during the clustering process. In the proposed method, the fuzzy ${c}$ -means the objective function is modified using a weighted Euclidean distance between the features with a monotonically decreasing function. The monotonically decreasing function helps control the features' contribution during the clustering process to partition the data into more relevant clusters. The proposed approach is validated, and performance is presented in various clustering performance measures on the different standard datasets. These clustering performance measures have also been compared with multiple state-of-the-art methods.

关键词： Gene expression Clustering algorithms partitioning algorithms Linear programming Weight measurement Nanobioscience Entropy K-medoids K-means alternate optimization cluster validation features fuzzy c-means gene expression

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Kmeans-CM Algorithm With Spectral Angle Mapper for Hyperspectral Image Classification

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IEEE ACCESS 2023年 11卷 26566-26576页

作者： Wei, Lin Ma, Huiyun Yin, Yuping Geng, Chao Liaoning Tech Univ Dept Basic Educ Huludao 125105 Peoples R China Liaoning Tech Univ Sch Elect & Informat Engn Huludao 125105 Peoples R China Liaoning Tech Univ Sch Elect & Control Engn Huludao 125105 Peoples R China

Hyperspectral images possess the characteristics of high dimensionality, which causes "dimensional disaster" and low classification accuracy. In order to solve the problems, based on traditional k-means algorithm, considering the importance of different bands for classification, and also combining both intra-class and inter-class information, a Kmeans-CM (K-means with correlation coefficient and maximize inter-class distance) algorithm with spectral angle mapper for hyperspectral image classification is proposed. First, we define weights of bands by introducing coefficient of variation and spectral angle mapping, which measures the importance of bands for classification, so as to make up for the deficiency of the traditional K-means algorithm to treat each band equally. Second, we introduce correlation coefficient to reset the intra-class distances in order to intensify correlation between pixels in the same category. Then, in order to reduce effect of local optimum of clustering effect, we introduce inter-class information for clustering by maximizing the distance between class centers and global center. Finally, the K-means clustering objective function is redefined according to the band weights and intra-class, inter-class information, and also solving optimally. The overall classification accuracy of the method reached 84.47%, 90.08% and 80.45% on classical hyperspectral data sets Pavia University, Salinas and Botswana respectively. And comparing with the traditional K-means algorithm, the CV-K-means algorithm and the CK-means algorithm, the experimental results show that the proposed algorithm can effectively improve classification accuracy of hyperspectral images, thus proving that the algorithm has good classification performance.

关键词： Classification algorithms Clustering algorithms Hyperspectral imaging Correlation partitioning algorithms Linear programming Entropy Band weighting correlation coefficient classification hyperspectral images K-means algorithm spectral angle mapper

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Network Embedding Based on Biased Random Walk for Community Detection in Attributed Networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第5期10卷 2279-2290页

作者： Guo, Kun Zhao, Zizheng Yu, Zhiyong Guo, Wenzhong Lin, Ronghua Tang, Yong Wu, Ling Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350108 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China

Community detection is a fundamental problem in complex network analysis that aims to find closely related groups of nodes. Recently, network embedding techniques have been integrated into community detection in two manners to capture the intricate relationships between nodes. The two-staged manner generates node embedding vectors and obtains communities by running a clustering algorithm on them. The single-staged manner simultaneously obtains node embedding vectors and communities by optimizing a hybrid objective concerning with node-community relationships. The general-purpose network embedding algorithms used in the first manner do not emphasize retaining node-community relationships. The second manner ignores the influence of a node's location in a community (at the center or boundary) and its attributes on community generation. In this article, we propose a biased-random-walk-based community detection (BRWCD) algorithm to tackle the issues. First, a topology-weighted degree is designed to enhance the random walk at the boundary of and inside a community to extract communities precisely. Second, we design an attribute-to-node influence index and an attribute-weighted degree to distinguish different attributes' influence on node transition to obtain communities with high internal cohesion. Comprehensive experiments on the real-world and synthetic networks demonstrate that BRWCD achieves nearly 10% higher accuracy at most than the state-of-the-art algorithms.

关键词： Clustering algorithms Topology Image edge detection Probabilistic logic Matrix decomposition Indexes partitioning algorithms Community detection complex network matrix factorization network embedding random walk

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Precise Encoding Complexity Control for Versatile Video Coding

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IEEE TRANSACTIONS ON BROADCASTING 2023年第1期69卷 33-48页

作者： Huang, Yan Xu, Jun Zhu, Chen Song, Li Zhang, Wenjun Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Network Engn MoE Key Lab Artificial Intelligence AI Inst Shanghai 200240 Peoples R China

Complexity reduction is a commonly used method to deal with complicated video coding standards, such as High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC). But the unstable performance under different video contents and Quantization Parameters (QPs) makes it difficult to precisely specify the target encoding time of every single sequence, which limits the practical use of the encoder. Inspired by rate control, in this paper, encoding time budget is regarded as a resource. We incorporate the hierarchical Group of Picture (GOP) encoding structure, the non-accelerated proportion, and block content variation within the frame, and design a top-down allocation and bottom-up feedback scheme, to achieve precise control of encoding complexity by controlling the maximum depth of QuadTree with nested Multi-type Tree (QTMT). In the scheme, Temporal ID (Tid)-based, Sum of Absolute Transformed Difference (SATD)-based methods and Linear (L) Model are designed to facilitate weighted allocation and feedback. The relationship between Planar Cost and encoding time is exploited as a Time-Cost (T-C) model, which guides the selection of Largest Coding Unit (LCU) encoding strategies in I-frames. For B-frames, we investigate a switching suppression assisted status-based method, to efficiently decide the encoding strategy of each LCU. Through the collaboration of the proposed technologies in the scheme, given any encoding time target achievable, encoding strategies will be automatically switched to help the actual encoding time gradually approach the target. To the best of our knowledge, this work is the first work on VVC-based encoding complexity control. The proposed scheme supports directly specifying the target encoding time or target Frame Per Second (FPS), and accurately realizing it within one pass. According to experimental results, under the target encoding time ratio of 80%, 60% and 40%, the average encoding time error is kept under 0.24%, 0.03% and 0.02% by our

关键词： Encoding Complexity theory Resource management partitioning algorithms Switches Transforms Prediction algorithms Versatile video coding (VVC) complexity control fast partition precise

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Large-Scale Meta-Heuristic Feature Selection Based on BPSO Assisted Rough Hypercuboid Approach

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第12期34卷 10889-10903页

作者： Luo, Chuan Wang, Sizhao Li, Tianrui Chen, Hongmei Lv, Jiancheng Yi, Zhang Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.

关键词： Feature extraction Rough sets Sparks Computational modeling partitioning algorithms Particle swarm optimization Heuristic algorithms Feature selection hypercuboid parallel computing particle swarm optimization rough sets

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DNN Migration in IoTs: Emerging Technologies, Current Challenges, and Open Research Directions

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IEEE CONSUMER ELECTRONICS MAGAZINE 2023年第3期12卷 28-38页

作者： Xue, Min Wu, Huaming Li, Ruidong Tianjin Univ Ctr Appl Math Tianjin Peoples R China Kanazawa Univ Inst Sci & Engn Kanazawa Japan

With the rapid development of the Internet of Things (IoT) and communication technology, deep neural network (DNN) applications, such as medical imaging, speech transcription, handwritten text recognition, have been widely used in IoT devices. However, due to resource constraints on these devices, e.g., limited memory capacity, weak computing capacity, and low battery capacity, IoT devices cannot support complicated DNN operation effectively and, thus, fail to fulfill the requirements of Quality of Service of mobile users. One promising approach is to migrate the DNN model to a remote cloud server to reduce the computing burden on IoT devices. Unfortunately, it still suffers from high delay and low bandwidth when communicating with cloud servers. Although the transmission delay of the edge server is low, its computing capacity lacks scalability and elasticity. To make matters worse, in the real world, the wireless connection between IoT devices and the cloud is intermittent, which can cause offloading failures during large-scale DNN data transmission. In this article, we describe a DNN model migration framework to overcome the abovementioned challenges, which consists of three parts: DNN model preprocessing, partition-offloading plan, and partition-uploading plan. Accordingly, we introduce the operation of the DNN migration and the available methods for each part. In addition, we improve the DNN partition-uploading plan in a multiuser edge-cloud collaborative computing environment. Finally, we highlight the important challenges of achieving more efficient DNN migration and point out the unresolved issues of DNN migration, which may shed light on future research directions.

关键词： Servers Computational modeling Cloud computing Delays Collaboration partitioning algorithms Task analysis DNN Partition Computation Offloading DNN Inference Edge-Cloud Collaborative Computing Intelligent Applications

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Large-Scale Clustering With Structured Optimal Bipartite Graph

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第8期45卷 9950-9963页

作者： Zhang, Han Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China

The widespread arising of data size gives rise to the necessity of undertaking large-scale data clustering tasks. To do so, the bipartite graph theory is frequently applied to design a scalable algorithm, which depicts the relations between samples and a few anchors, instead of binding pairwise samples. However, the bipartite graphs and existing spectral embedding methods ignore the explicit cluster structure learning. They have to obtain cluster labels by using post-processing like K-Means. More than that, existing anchor-based approaches always acquire anchors by using centroids of K-Means or a few random samples, both of which are time-saving but performance-unstable. In this paper, we investigate the scalability, stableness and integration in large-scale graph clustering. We propose a cluster-structured graph learning model, thus obtaining a c-connected (c is the cluster number) bipartite graph and also getting discrete labels straightforward. Taking data feature or pairwise relation as a start point, we further design an initialization-independent anchor selection strategy. Experimental results reported for synthetic and real-world datasets demonstrate the proposed method outperforms its peers.

关键词： Bipartite graph Scalability Task analysis Clustering algorithms Optimization Laplace equations partitioning algorithms Anchor selection bipartite graph discrete labels large-scale clustering pairwise relation

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Multiple Kernel Clustering With Compressed Subspace Alignment

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第1期34卷 252-263页

作者： Zhou, Sihang Ou, Qiyuan Liu, Xinwang Wang, Siqi Liu, Luyan Wang, Siwei Zhu, En Yin, Jianping Xu, Xin Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China Tencent Jarvis Lab Shenzhen 518057 Peoples R China Dongguan Univ Technol Sch Cyberspace Sci Dongguan 523808 Guangdong Peoples R China

Multiple kernel clustering (MKC) has recently achieved remarkable progress in fusing multisource information to boost the clustering performance. However, the O( n(2)) memory consumption and O( n(3) ) computational complexity prohibit these methods from being applied into median- or large-scale applications, where n denotes the number of samples. To address these issues, we carefully redesign the formulation of subspace segmentation-based MKC, which reduces the memory and computational complexity to O( n) and O( n(2) ), respectively. The proposed algorithm adopts a novel sampling strategy to enhance the performance and accelerate the speed of MKC. Specifically, we first mathematically model the sampling process and then learn it simultaneously during the procedure of information fusion. By this way, the generated anchor point set can better serve data reconstruction across different views, leading to improved discriminative capability of the reconstruction matrix and boosted clustering performance. Although the integrated sampling process makes the proposed algorithm less efficient than the linear complexity algorithms, the elaborate formulation makes our algorithm straightforward for parallelization. Through the acceleration of GPU and multicore techniques, our algorithm achieves superior performance against the compared state-of-the-art methods on six datasets with comparable time cost to the linear complexity algorithms.

关键词： Kernel Clustering algorithms Prediction algorithms partitioning algorithms Optimization Memory management Sparse matrices Compressed subspace alignment multiple kernel clustering (MKC) sampling process modeling

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Density-Based Clustering by Means of Bridge Point Identification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第11期35卷 11274-11287页

作者： Colomba, Luca Cagliero, Luca Garza, Paolo Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

Density-based clustering focuses on defining clusters consisting of contiguous regions characterized by similar densities of points. Traditional approaches identify core points first, whereas more recent ones initially identify the cluster borders and then propagate cluster labels within the delimited regions. Both strategies encounter issues in presence of multi-density regions or when clusters are characterized by noisy borders. To overcome the above issues, we present a new clustering algorithm that relies on the concept of bridge point. A bridge point is a point whose neighborhood includes points of different clusters. The key idea is to use bridge points, rather than border points, to partition points into clusters. We have proved that a correct bridge point identification yields a cluster separation consistent with the expectation. To correctly identify bridge points in absence of a priori cluster information we leverage an established unsupervised outlier detection algorithm. Specifically, we empirically show that, in most cases, the detected outliers are actually a superset of the bridge point set. Therefore, to define clusters we spread cluster labels like a wildfire until an outlier, acting as a candidate bridge point, is reached. The proposed algorithm performs statistically better than state-of-the-art methods on a large set of benchmark datasets and is particularly robust to the presence of intra-cluster multiple densities and noisy borders.

关键词： Bridges Clustering algorithms partitioning algorithms Noise measurement Anomaly detection Benchmark testing Optics Density-based clustering outlier detection unsupervised learning

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