Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

中文翻译：

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用于多媒体数据分析的自适应加权多视图深基矩阵分解

特征表示学习是人工智能研究中的一个关键问题。多视图多媒体数据可以提供丰富的信息，这使得特征表示成为当前数据分析的研究热点之一。最近，已经提出了大量的多视图数据特征表示方法，其中矩阵分解表现出优异的性能。因此，我们提出了一种将矩阵分解、深度学习和视图融合集成在一起的自适应加权多视图深度基矩阵分解 (AMDBMF) 方法。具体来说，我们首先对每个视图的数据进行深度基矩阵分解。然后，将所有视图整合起来，完成多视图特征学习的过程。最后，我们提出了一种自适应加权策略来融合每个视图的低维特征，以便可以为多视图多媒体数据获得统一的特征表示。我们还设计了一个迭代更新算法来优化目标函数并通过数值实验证明优化算法的收敛性。我们对五个多视图多媒体数据集进行了聚类实验，并将所提出的方法与当前几种优秀的方法进行了比较。实验结果表明，该方法的聚类性能优于其他比较方法。我们还设计了一个迭代更新算法来优化目标函数并通过数值实验证明优化算法的收敛性。我们对五个多视图多媒体数据集进行了聚类实验，并将所提出的方法与当前几种优秀的方法进行了比较。实验结果表明，该方法的聚类性能优于其他比较方法。我们还设计了一个迭代更新算法来优化目标函数并通过数值实验证明优化算法的收敛性。我们对五个多视图多媒体数据集进行了聚类实验，并将所提出的方法与当前几种优秀的方法进行了比较。实验结果表明，该方法的聚类性能优于其他比较方法。