论文标题
通过饱和坐标下降的有效非负张量分解
Efficient Nonnegative Tensor Factorization via Saturating Coordinate Descent
论文作者
论文摘要
随着计算技术和基于Web的应用程序的进步，数据以多维形式越来越多地生成。由于存在大量用户和较少的用户交互，因此这些数据通常很少。为了解决这个问题，基于非负张量分解（NTF）方法已被广泛使用。但是，现有的分解算法在张量的大小，密度和等级的所有三个条件下都不适合处理。因此，它们的适用性变得有限。在本文中，我们使用元素选择方法提出了一种新颖的快速有效的NTF算法。我们使用Lipschitz的连续性计算元素的重要性，并提出了一种基于饱和点的元素选择方法，该方法选择了一组元素列以更新以解决优化问题。经验分析表明，与相关的最新算法相比，所提出的算法可以根据张量，密度和等级而扩展。
With the advancements in computing technology and web-based applications, data is increasingly generated in multi-dimensional form. This data is usually sparse due to the presence of a large number of users and fewer user interactions. To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process in all three conditions of size, density, and rank of the tensor. Consequently, their applicability becomes limited. In this paper, we propose a novel fast and efficient NTF algorithm using the element selection approach. We calculate the element importance using Lipschitz continuity and propose a saturation point based element selection method that chooses a set of elements column-wise for updating to solve the optimization problem. Empirical analysis reveals that the proposed algorithm is scalable in terms of tensor size, density, and rank in comparison to the relevant state-of-the-art algorithms.
