Results 41 to 50 of about 223,331 (198)

Temporal Tensor Factorization for Multidimensional Forecasting [PDF]

, 2023
In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix
Cates, Jackson, Caudle, Kyle, Scipke, Karissa, Hoover, Randy   +3 more
core  

Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values

, 2021
© 2021 ACM.Given a time-evolving tensor stream with missing values, how can we accurately discover latent factors in an online manner to predict missing values?
Ahn, Dawon, Kang, U, Kim, Seyun
core   +1 more source

A hybrid Bayesian network and tensor factorization approach for missing value imputation to improve breast cancer recurrence prediction

Journal of King Saud University: Computer and Information Sciences, 2019
Data mining and machine learning approaches can be used to predict breast cancer recurrence. However, real datasets often include missing values for various reasons.
Mahin Vazifehdan, Mohammad Hossein Moattar, Mehrdad Jalali   +2 more
doaj   +1 more source

Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting [PDF]

, 2017
Tensor factorization with hard and/or soft constraints has played an important role in signal processing and data analysis. However, existing algorithms for constrained tensor factorization have two drawbacks: (i) they require matrix-inversion; and (ii ...
Ono, Shunsuke, Kasai, Takuma
core   +1 more source

Tensor Factorization Using Auxiliary Information [PDF]

Data Mining and Knowledge Discovery, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima   +3 more
openaire   +3 more sources

The factorization of rank tensors

Duke Mathematical Journal, 1943
Not ...
openaire   +2 more sources

Missing value imputation for breast cancer diagnosis data using tensor factorization improved by enhanced reduced adaptive particle swarm optimization

Journal of King Saud University: Computer and Information Sciences, 2019
Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis.
Atefeh Nekouie, Mohammad Hossein Moattar
doaj   +1 more source

Robust Tensor Factorization for Color Image and Grayscale Video Recovery

IEEE Access, 2020
Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory.
Shiqiang Du, Yuqing Shi, Wenjin Hu, Weilan Wang, Jing Lian   +4 more
doaj   +1 more source

Noisy tensor completion for tensors with a sparse canonical polyadic factor [PDF]

2017 IEEE International Symposium on Information Theory (ISIT), 2017
In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with one of the factors being sparse. We present general theoretical error bounds for an estimate obtained by using a complexity-regularized maximum likelihood principle and then instantiate these bounds for ...
Swayambhoo Jain, Alexander Gutierrez, Jarvis D. Haupt   +2 more
openaire   +3 more sources

Factor Augmented Tensor-on-Tensor Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence
This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitrary tensor order and data dimension. Existing methods either focused on linear models without accounting for possibly nonlinear relationships between covariates and responses,
Guanhao Zhou, Yuefeng Han, Xiufan Yu
openaire   +3 more sources