Multi-view deep reciprocal nonnegative matrix factorization

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

Abstract

Multi-view deep matrix factorization has recently gained popularity for extracting high-quality representations from multi-view data to improve the processing performance of multi-view data in pattern recognition, data mining, and machine learning. It explores the hierarchical semantics of data by performing a multi-layer decomposition on representation matrices after decomposing the data into basis and representation matrices but ignoring the basis matrices, which also contain valuable information about data. Extracting high-quality bases during the deep representation learning process can facilitate the learning of high-quality representations for multi-view data. To this end, this paper proposes a novel deep nonnegative matrix factorization architecture, named Multi-view Deep Reciprocal Nonnegative Matrix Factorization (MDRNMF), that collaborates with high-quality basis extraction, allowing the deep representation learning and high-quality basis extraction to promote each other. Based on the representations at the top layer, this paper adaptively learns the intrinsic local similarities of data within each view to capture the view-specific information. In addition, to explore the high-order data consistency across views, this paper introduces a Schatten p-norm-based low-rank regularization on the similarity tensor stacked by the view-specific similarity matrices. In this way, the proposed method can effectively explore and leverage the view-specific and consistent information of multi-view data simultaneously. Finally, extensive experiments demonstrate the superiority of the proposed model over several state-of-the-art methods.
多视角深度倒数非负矩阵因式分解
多视图深度矩阵因式分解最近很受欢迎,它可以从多视图数据中提取高质量的表示,从而在模式识别、数据挖掘和机器学习中提高多视图数据的处理性能。它在将数据分解为基础矩阵和表示矩阵后,通过对表示矩阵进行多层分解来探索数据的层次语义,但忽略了基础矩阵,而基础矩阵也包含数据的宝贵信息。在深度表示学习过程中提取高质量的基,可以促进多视图数据的高质量表示学习。为此,本文提出了一种新颖的深度非负矩阵因式分解架构,名为多视图深度互易非负矩阵因式分解(MDRNMF),该架构与高质量基提取相结合,使深度表示学习与高质量基提取相互促进。在顶层表示的基础上,本文自适应地学习每个视图中数据的内在局部相似性,以捕捉视图的特定信息。此外,为了探索跨视图的高阶数据一致性,本文在由视图特定相似性矩阵堆叠的相似性张量上引入了基于 Schatten p-norm 的低秩正则化。这样,本文提出的方法就能同时有效地探索和利用多视图数据的视图特定信息和一致性信息。最后,大量实验证明了所提出的模型优于几种最先进的方法。
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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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