Latent temporal smoothness-induced Schatten-p norm factorization for sequential subspace clustering

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuan Xu , Zhen-Zhen Zhao , Tong-Wei Lu , Wei Ke , Yi Luo , Yan-Lin He , Qun-Xiong Zhu , Yang Zhang , Ming-Qing Zhang
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引用次数: 0

Abstract

This paper presents an innovative latent temporal smoothness-induced Schatten-p norm factorization (SpFLTS) method aimed at addressing challenges in sequential subspace clustering tasks. Globally, SpFLTS employs a low-rank subspace clustering framework based on Schatten-2/3 norm factorization to enhance the comprehensive capture of the original data features. Locally, a total variation smoothing term is induced to the temporal gradients of latent subspace matrices obtained from sub-orthogonal projections, thereby preserving smoothness in the sequential latent space. To efficiently solve the closed-form optimization problem, a fast Fourier transform is combined with the non-convex alternating direction method of multipliers to optimize latent subspace matrix, which greatly speeds up computation. Experimental results demonstrate that the proposed SpFLTS method surpasses existing techniques on multiple benchmark databases, highlighting its superior clustering performance and extensive application potential.
用于序列子空间聚类的潜在时间平滑性诱导 Schatten-p norm 因式分解法
本文提出了一种创新的潜在时态平滑诱导 Schatten-p norm 因式分解(SpFLTS)方法,旨在解决顺序子空间聚类任务中的难题。从全局来看,SpFLTS 采用了基于 Schatten-2/3 准则因式分解的低秩子空间聚类框架,以增强对原始数据特征的全面捕捉。从局部来看,通过次正交投影得到的潜在子空间矩阵的时间梯度诱导了总变异平滑项,从而保持了序列潜在空间的平滑性。为了有效解决闭式优化问题,快速傅立叶变换与非凸交替方向乘法相结合来优化潜子空间矩阵,从而大大加快了计算速度。实验结果表明,所提出的 SpFLTS 方法在多个基准数据库上超越了现有技术,凸显了其卓越的聚类性能和广泛的应用潜力。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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