Angular Reconstructive Discrete Embedding With Fusion Similarity for Multi-View Clustering

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-29 DOI:10.1109/TKDE.2024.3487907
Jintang Bian;Xiaohua Xie;Chang-Dong Wang;Lingxiao Yang;Jian-Huang Lai;Feiping Nie
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Abstract

Effectively and efficiently mining valuable clustering patterns is a challenging problem when handling large-scale data from diverse sources. Existing approaches adopt anchor graph learning or binary representation embedding to reduce computational complexity. Normally, anchor graph learning can not directly obtain the clustering assignment except adopt the post-processing stage, such as graph cut or k-means clustering. The binary representation embedding neglects the structure information in Hamming space. In order to overcome these limitations, this paper proposes a novel, effective, and efficient angular reconstructive discrete embedding method with fusion similarity for a multi-view clustering (AFMC) that can jointly learn the global and local structure preserving binary representation and clustering assignment. Specifically, we propose to use angular reconstructive error minimization to maintain the global similarity correlation of binary representations of heterogeneous features in a common Hamming space. Moreover, we design a multi-view discrete ridge regression with fusion similarity term to handle the out-of-sample problem and preserve the local manifold structure. In addition, we propose an efficient optimization algorithm with linear computational complexity to solve the non-convex and non-smooth objective function. The experimental results demonstrate that AFMC outperforms several state-of-the-art large-scale multi-view clustering methods.
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多视图聚类的角度重构离散嵌入与融合相似性
在处理来自不同来源的大规模数据时,有效和高效地挖掘有价值的聚类模式是一个具有挑战性的问题。现有的方法采用锚图学习或二值表示嵌入来降低计算复杂度。锚点图学习通常不能直接获得聚类分配,只能采用图割或k-means聚类等后处理阶段。二值表示嵌入忽略了汉明空间中的结构信息。为了克服这些局限性,本文提出了一种新颖、有效、高效的多视图聚类(AFMC)的融合相似度角重构离散嵌入方法,该方法可以共同学习全局和局部结构,并保持二值表示和聚类分配。具体而言,我们提出使用角重构误差最小化来保持异构特征二进制表示在公共Hamming空间中的全局相似相关性。此外,我们设计了一种带有融合相似项的多视图离散脊回归来处理样本外问题并保留局部流形结构。此外,我们提出了一种具有线性计算复杂度的高效优化算法来求解非凸非光滑目标函数。实验结果表明,AFMC优于几种最先进的大规模多视图聚类方法。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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