By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set. Non-negative Matrix Factorization (NMF) is a data mining technique that splits data matrices by imposing restrictions on the elements' non-negativity into two matrices: one representing the data partitions and the other to represent the cluster prototypes of the data set. This method has attracted a lot of attention and is used in a wide range of applications, including text mining, clustering, language modeling, music transcription, and neuroscience (gene separation). The interpretation of the generated matrices is made simpler by the absence of negative values. In this article, we propose a study on multi-modal clustering algorithms and present a novel method called multi-modal multi-view non-negative matrix factorization, in which we analyze the collaboration of several local NMF models. The experimental results show the value of the proposed approach, which was evaluated using a variety of data sets, and the obtained results are very promising compared to state of art methods.

翻译：通过关联相关对象，无监督机器学习技术旨在揭示数据集中的潜在模式。非负矩阵分解（NMF）是一种数据挖掘技术，通过对矩阵元素的非负性施加约束，将数据矩阵分解为两个矩阵：一个表示数据划分，另一个表示数据集的聚类原型。该方法备受关注，广泛应用于文本挖掘、聚类、语言建模、音乐转录和神经科学（基因分离）等领域。由于分解矩阵不含负值，其生成结果更易于解释。本文针对多模态聚类算法展开研究，并提出一种名为多模态多视图非负矩阵分解的新方法，通过分析多个局部NMF模型的协作机制实现聚类。基于多种数据集的实验结果表明，所提方法具有显著价值，与现有先进方法相比，得到的实验结果极具竞争力。