Tensor completion is a problem of filling the missing or unobserved entries of partially
observed tensors. Due to the multidimensional character of tensors in describing complex …

Multiway data-related learning tasks pose a huge challenge to the traditional regression
analysis techniques due to the existence of multidirectional relatedness. Simply vectorizing …

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor
matrices to obtain an optimal low-rank representation feature for the High-Order, High …

In the past few decades, there has been rapid growth in quantity and variety of healthcare
data. These large sets of data are usually high dimensional (eg patients, their diagnoses …

Canonical Polyadic Decomposition (CPD) of sparse tensors is an effective tool in various
machine learning and data analytics applications, in which sparse Matricized Tensor Times …

Network telemetry, characterized by its efficient push model and high-performance
communication protocol (gRPC), offers a new avenue for collecting fine-grained real-time …

Tensor decomposition is a fundamental multi-dimensional data analysis tool for many data-
driven applications. However, the rapidly increasing data requires an efficient distributed …

摘要随着社交网络, 电商系统, 移动终端设备的快速发展, 海量且高维的数据正以前所未有的速度
不断地增长和积累. 高维数据可以自然地表示为张量. 张量的Tucker 分解方法是一种常用且经典 …

We consider a Canonical Polyadic (CP) decomposition approach to low-rank tensor
completion (LRTC) by incorporating external pairwise similarity relations through graph …

Abstract Purpose: We propose InParTen2, a multi-aspect parallel factor analysis three-
dimensional tensor decomposition algorithm based on the Apache Spark framework. The …