Leveraging stochastic gradient descent iterations, a scalable, online algorithm is developed to learn the decomposition factors on-the-fly and perform data ...
Leveraging stochastic gradient descent iterations, a scalable, online algorithm is developed to learn the decomposition factors on-the-fly and perform data ...
Online Tensor Decomposition and Imputation for Count Data. 1. Page 2. Tensors and PARAFAC decomposition. ▻ Three-way tensor X ∈ RM×N×T . PARAFAC decomposition.
A scalable, online algorithm is developed to learn the decomposition factors on-the-fly and perform data imputation as a byproduct, and preliminary ...
It is suitable for both sparse and dense tensors. SGD-based methods [28], [30] have also been developed for online CPD. Specifically, Ye et al.
Apr 11, 2018 · We present a method, PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition (PREDICTD), to computationally impute missing experiments.
Abstract—Tensor decomposition is a fundamental tool for multiway data analysis. While most decomposition algorithms operate a collection of static data and ...
Dec 27, 2024 · Tensor decomposition (TD) models have been well established as a natural and powerful way of representing systems and data that involve ...
Nonparametric tensor ring decomposition with scalable ...
www.sciencedirect.com › article › abs › pii
Tensor decompositions (TDs) are fundamental tools for modeling data with multiple dimensions, i.e., video (time × channel × width × height), spatiotemporal data ...
Tensor decomposition when applied to higher-order data can extract features, which in turn can be used for prediction tasks such as classification. Additionally ...