A stochastic algorithm for the ParaTuck decomposition

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-09-17 DOI:10.1016/j.dsp.2024.104767

引用次数: 0

Abstract

This paper introduces a novel stochastic algorithm for the ParaTuck Decomposition (PTD), addressing the challenge of local minima encountered in the traditional alternating least squares (ALS) approach. The proposed method integrates stochastic steps into the ALS framework to avoid the common swamp problems, where numerical difficulties prevent accurate decompositions. Our simulations indicate good convergence properties for PTD, suggesting a potential increase in the efficiency and reliability of this tensor decomposition across various applications.

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ParaTuck 分解的随机算法

本文为 ParaTuck 分解（PTD）引入了一种新型随机算法，解决了传统交替最小二乘法（ALS）中遇到的局部最小值难题。所提出的方法将随机步骤集成到 ALS 框架中，以避免常见的沼泽问题，在沼泽问题中，数值困难阻碍了精确分解。我们的模拟结果表明，PTD 具有良好的收敛特性，表明这种张量分解方法在各种应用中的效率和可靠性都有可能得到提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,

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